Intelligent building management systems

ABSTRACT

A hierarchical resource management system for a building includes one or more processors. The processors implement a plurality of agents that each monitor sensed values, and generate operating scenarios based on the sensed values for corresponding resources. The processors also implement a coordinator that filters the operating scenarios to remove the operating scenarios that violate internal laws of the agents to form an aggregate validated set of operating scenarios. The processors further implement a supervisor that, responsive to receipt of target conditions for the zones and the aggregate validated set of operating scenarios from the coordinator, selects a combination of the operating scenarios from the aggregate validated set of operating scenarios that achieves target conditions and minimizes overall energy consumption by the resources such that some of the operating scenarios of the combination do not minimize energy consumption of the resources corresponding to the some of the operating scenarios.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 62/682,746, filed Jun. 8, 2018, which is incorporated byreference herein in its entirety.

TECHNICAL FIELD

This disclosure relates to the control of equipment used withinbuildings.

BACKGROUND

A building management system (BMS), otherwise known as a buildingautomation system (BAS), is a computer-based control system installed ina building that controls and monitors the building's electrical andmechanical equipment such as ventilation, lighting, power systems, firesystems, and security systems. As such, a BMS may also include a varietyof devices (e.g., HVAC devices, controllers, chillers, fans, sensors,lighting controllers, lighting fixtures etc.) configured to facilitatemonitoring and controlling the building space. Throughout thisdisclosure, such devices are referred to as BMS devices or buildingequipment.

Typically, even though the building controllers, input-output devices,and various switching equipment communicate via open source networkssuch as BACnet, LONworks, Modbus etc. the programming language for eachsuch device is proprietary to the specific manufacturer. The sequencesof operation for each system are manually programmed into eachcontroller and then “released” to automatically control their relatedsystems.

SUMMARY

A hierarchical resource management system for a building, that has aplurality of zones each with a corresponding resource arranged to alteran environment of the zone, includes one or more processors. Theprocessors implement a plurality of agents that each monitor sensedvalues describing conditions of one of the zones, generate operatingscenarios based on the sensed values for the resource corresponding tothe one of the zones, and operate the resource according to a commandedone of the operating scenarios. Each of the operating scenarios describean array of set point values for the resource and correspond to anenergy consumption of the resource. The processors also implement acoordinator that, responsive to receipt of the operating scenarios fromeach of the agents, filters the operating scenarios to remove theoperating scenarios that violate internal laws of the agents to form anaggregate validated set of operating scenarios. The processors furtherimplement a central brain that, responsive to receipt of targetconditions for the zones and the aggregate validated set of operatingscenarios from the coordinator, selects a combination of the operatingscenarios from the aggregate validated set of operating scenarios thatachieves the target conditions and minimizes overall energy consumptionby the resources such that some of the operating scenarios of thecombination do not minimize energy consumption of the resourcescorresponding to the some of the operating scenarios, and directs thecoordinator to command operation of the resources according to thecombination of the operating scenarios.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the communication architecture between SMITHGROUP-AI andvarious system coordinators.

FIG. 2 shows the internal structure of SMITHGROUP-AI and its relatedenvironment.

FIG. 3 shows the internal structure of the zone agent and its relatedenvironment.

FIG. 4 shows the communication architecture between the various agentsand coordinators.

FIG. 5 shows the internal structure of the power and lighting systemcoordinator and its related environment.

FIG. 6 shows the internal structure of the panelboard agent and itsrelated environment.

FIG. 7 shows the internal structure of the renewable energy agent andits related environment.

FIG. 8 shows an airside system as having one air handling unitdelivering a mixture of outside air and return air to five zones.

FIG. 9 shows the communication architecture between the various airsidesystem agents.

FIG. 10 shows the internal structure of the AHU system coordinator andits related environment.

FIG. 11 shows the internal structure of the AHU agent and its relatedenvironment.

FIG. 12 shows the heating plant providing heating hot water to one airhandling unit and seven thermal zones.

FIG. 13 shows the communication architecture between the various hotwater system agents.

FIG. 14 shows the internal structure of the hot water system (HWS)coordinator and its environment.

FIG. 15 shows the internal structure of the heating plant agent and itsrelated environment.

FIG. 16 shows a chilled water system including four chilled water pumps,pumped in parallel, one waterside economizer heat exchanger, and threechillers.

FIG. 17 shows the communication architecture between the various chilledwater system agents.

FIG. 18 shows the internal structure of the chilled water systemcoordinator and its related environment.

FIG. 19 shows the internal structure of the chilled water plant agentand its related environment.

FIG. 20 shows a condenser water system including four condenser waterpumps and three cooling towers, each with two cells.

FIG. 21 shows the communication architecture between the variouscondenser water system agents.

FIG. 22 shows the internal structure of the condenser water systemcoordinator and its related environment.

FIG. 23 shows the internal structure of the condenser water plant agentand its related environment.

DETAILED DESCRIPTION

Various embodiments of the present disclosure are described herein.However, the disclosed embodiments are merely exemplary and otherembodiments may take various and alternative forms that are notexplicitly illustrated or described. The figures are not necessarily toscale; some features may be exaggerated or minimized to show details ofparticular components. Therefore, specific structural and functionaldetails disclosed herein are not to be interpreted as limiting, butmerely as a representative basis for teaching one of ordinary skill inthe art to variously employ the present invention. As those of ordinaryskill in the art will understand, various features illustrated anddescribed with reference to any one of the figures may be combined withfeatures illustrated in one or more other figures to produce embodimentsthat are not explicitly illustrated or described. The combinations offeatures illustrated provide representative embodiments for typicalapplications. However, various combinations and modifications of thefeatures consistent.

A. SMITHGROUP-AI

1. General Description

SMITHGROUP-AI (supervisor) is an independent, multifunctional softwareagent responsible for the monitoring and control of all agents thatcontrol all building systems. Its main goal is to direct the agents tooperate at conditions that result in the lowest possible building energyconsumption levels and building energy cost levels. This is achieved byanalyzing all possible combinations and associated laws between thevarious system coordinator scenarios, and then directing each systemcoordinator to implement a scenario that will result in the lowestpossible building energy consumption levels. It is not required norassumed that SMITHGROUP-AI selects the most energy efficient scenariofrom each system coordinator. Some system coordinator scenarios selectedto be implemented by SMITHGROUP-AI might not be the most energy costefficient for that system; however, when analyzed from an overallbuilding energy consumption or energy cost level, those scenarios arecollectively the most energy efficient. Further, SMITHGROUP-AI usingvarious known machine learning algorithms may predict the overallbuilding energy consumption and energy cost levels for the followinghour, day, week, month and year.

FIG. 1 shows the communication architecture between SMITHGROUP-AI 10 andthe various system coordinators 12, 14, 16, 18, 20, 22.

2. Internal Structure

Referring to FIG. 2, the internal structure of SMITHGROUP-AI 10 and itsrelated environment is shown. The environment for SMITHGROUP-AI 10 iscomprised of all system coordinators 12, 14, 16, 18, 20, 22 that itmonitors and controls.

SMITHGROUP-AI 10 is comprised of five modules, each with its owndedicated algorithms and controls logic. The data filtering module 24 isresponsible for separating the data received from the variouscoordinators 12, 14, 16, 18, 20, 22. For example, the actual buildingenergy consumption and energy cost levels may be sent to the systemfeedback module 26, while energy consumption predictions and associatedscenarios from the system coordinators 12, 14, 16, 18, 20, 22 will besent to the system analysis and control module 28.

The system feedback module 26 is responsible for the following:

-   -   Collection of information from the system coordinators 12, 14,        16, 18, 20, 22; the information may represent status (e.g.        failed, maintenance), or actual agent energy consumption and        energy cost levels. A failed status may indicate that a system        coordinator may no longer control its environment and should be        excluded from the overall building energy consumption and energy        cost level predictions that the machine learning module 30 is        making A maintenance status may indicate that a system        coordinator has entered or may enter a maintenance mode and that        the machine learning module 30 needs to update its predictions        accordingly. The information may also represent predictions        related to the energy consumption levels of each agent.    -   Analysis of predictions related to the total building energy        consumption and energy cost levels; the actual energy        consumption levels from the various agents will be compared        against their own predictions and also against the predictions        made by the machine learning module 30. Any large discrepancies        between the predicted and actual energy consumption and energy        cost levels could mean that the associated scenarios that        SMITHGROUP-AI 10 has directed the system coordinators 12, 14,        16, 18, 20, 22 to execute are no longer valid. This could also        indicate that, due to various events (e.g. equipment failures,        sensor failures etc.) some agents within the system may conflict        with their own internal laws. Further, this could also indicate        that the difference in the error or accuracy of the predictions        made by the machine learning module 30 or by each system        coordinator 12, 14, 16, 18, 20, 22 and the actual outcome might        have exceeded critical limits, which in turn has affected the        overall energy consumption and energy cost levels of the        building. The system feedback module 26 may then send the actual        energy consumption and energy cost levels to the machine        learning module 30 such that the machine learning module 30 can        update its internal learning algorithms.    -   An internal law for an agent may be one (or both) of the        following (i) a physical property (e.g. maximum air flow through        a fan, maximum water flow through a pump) of a system that the        agent is controlling and/or responsible for, (ii) a software or        hardware (e.g. computing power) limitation of the agent; e.g. a        coordinator may ask an agent to provide predictions for a        significantly large (and maybe unrealistic) number of scenarios        within a limited/short amount of time. This request may impede        the agent's ability to control the system that it serves. Agents        may report their internal laws to their coordinators such that        the coordinators can determine whether certain scenarios        generated by one agent violates the internal laws of another        agent, etc.    -   Receiving requests from the HUMAN INTERVENTION Application        Programming Interface (API) 32. For example, the system feedback        module 26 may receive a request to simulate what will the total        building energy cost (e.g. natural gas or electricity) be if the        cost of electricity will increase by 20% starting two months        from the current date. The system feedback module 26 will then        send this scenario to the machine learning module 30, which in        turn may use its own internal algorithms to make predictions        regarding the total building energy costs; the machine learning        module 30 will then send these predictions back to the system        feedback module 26, which in turn will send them to the HUMAN        INTERFACE API 32. The machine learning module 30 may also        determine that, to make the requested predictions, it needs        inputs (e.g. energy consumption predictions) from the various        system coordinators 12, 14, 16, 18, 20, 22; as such, it may ask        the system analysis and control module 28 to direct the various        system coordinators 12, 14, 16, 18, 20, 22 to make the        associated predictions and send them back to SMITHGROUP-AI 10 to        be processed and analyzed. Further, the system feedback module        26 may receive a request to simulate what will happen (e.g. what        systems are affected and by what degree) if a major piece of        equipment (e.g. chiller, boiler) will fail at a certain moment        in time. The system feedback module 26 and the machine learning        module 30 will then follow a process as described in the        previous example.    -   Receiving commands from the HUMAN INTERVENTION API 32. For        example, the system feedback module 26 may receive a command to        run the entire chilled water plant at 50% capacity or to put a        certain piece of equipment (e.g. chiller, boiler, AHU, etc.) in        maintenance mode. The system feedback module 26 will then send        this command to the system analysis and control module 28 to be        analyzed. Before executing the command, the system analysis and        control module 28 will direct the system coordinators 12, 14,        16, 18, 20, 22 to simulate what will be the outcomes (e.g.        unable to maintain zone temperatures, warmer chilled water        temperature, colder hot water temperature, etc.) associated with        the command. The System Analysis and Control module 28 will then        send these outcomes to the System Feedback module 26, which in        turn will send them back to the HUMAN INTERVENTION API 32 for        confirmation. Once it receives confirmation from the HUMAN        INTERVENTION API 32, via the system feedback module 26, the        system analysis and control module 28 will then direct the        system coordinators 12, 14, 16, 18, 20, 22 to execute the        command.

The machine learning module 30 is responsible for the following:

-   -   Collection of information from the system feedback module 26.        The type of information that this module will collect is        described under the system feedback module 26.    -   Collection of information from the system analysis and control        module 28. This information may represent prediction scenarios        from system coordinators 12, 14, 16, 18, 20, 22 regarding energy        consumption levels associated with each system.    -   receiving prediction requests from the system analysis and        control module 28. For example, the system analysis and control        module 28, after it analyzed and established what combination of        system coordinator scenarios will need to be executed, may send        these scenarios to the machine learning module 30. The machine        learning module 30, using machine learning algorithms and        historical data, may predict that the combination of system        coordinator scenarios selected by the system analysis and        control module 28 will not result in the lowest possible energy        consumption and energy cost levels for the entire building. The        machine learning module 30 will then send this output to the        system analysis and control module 28 for analysis.    -   Making predictions, using machine learning algorithms, based on        the data received from the system analysis and control module        28, for the overall building energy consumption or energy cost        levels. In addition, the machine learning module 30 may also        make predictions related to the peak electricity demand for the        entire building. For example, by analyzing the electricity peak        demand charge rate schedule from the utility provider, and with        input from the system analysis and control module 28, the        machine learning module 30 may predict that, at a certain time        in the day, the facility peak electricity demand may exceed a        value after which the facility will incur significant        surcharges. The machine learning module 30 may then send this        prediction to the system analysis and control module 28, which        in turn may direct the system coordinators 12, 14, 16, 18, 20,        22 to analyze various scenarios for their related systems and        agents to minimize the overall peak facility electricity demand        at that time in the day. The systems coordinators 12, 14, 16,        18, 20, 22 will then send back their associated predictions,        together with any system constraints or laws that they might not        be able to meet should they be directed to implement the        associated scenarios. For example, AHU-1 system coordinator 20        may determine that, to reduce the energy consumption of the AHU        fans, some zones, based on their importance factor, may have to        have their temperature setpoints increased or decreased. (zone        and system are sometimes used interchangeably herein.) This        approach could put a zone agent in conflict with its own        internal laws. The system analysis and control module 28 will        then analyze the predicted scenarios received from the AHU-1        system coordinator 20 and it may decide to direct the system        coordinators 12, 14, 16, 18, 20, 22 to execute the scenarios        associated with the combination that result in a peak facility        electricity demand lower than the critical value, even though        this command may put various agents in conflict with their own        internal laws.    -   Sending the predictions to the system analysis and control        module 28 and to the scenario generator module 34.

The system analysis and control module 28 is responsible for thefollowing:

-   -   Monitoring and control of all system coordinators 12, 14, 16,        18, 20, 22.    -   Collection of information from the data filtering module 24. The        type of information that this module will collect is described        under the data filtering module 24. The system analysis and        control module 28 may then analyze all possible system        coordinator scenario combinations and select the combination        that will result in the lowest possible building energy        consumption or energy cost levels. After establishing the        combination that will result in the lowest possible building        energy consumption or energy cost levels, the system analysis        and control module 28 may direct the system coordinators 12, 14,        16, 18, 20, 22 to execute the scenarios associated with that        specific combination.    -   Collection of information from the system feedback module 26.        The type of information that this module will collect is        described under the system feedback module 26.    -   Receiving predictions made by the machine learning module 30.        The type of predictions that the machine learning module 30 may        send to the system analysis and control module 28 is described        in the machine learning module 30 section above. Based on the        input (e.g. equipment failures, unoccupied zones previously        predicted to be occupied zones, etc.) provided by the system        coordinators 12, 14, 16, 18, 20, 22, the system analysis and        control module 28 may decide to override the predictions made by        the machine learning module 30 and direct the system        coordinators 12, 14, 16, 18, 20, 22 to implement the combination        of scenarios previously established. The system analysis and        control module 28 may also decide to direct the system        coordinators 12, 14, 16, 18, 20, 22 to update their predictions        and send them back to SMITHGROUP-AI 10 for analysis.

The scenario generator module 34 is responsible for continuously lookingfor ways to improve the overall energy or energy cost performance of thebuilding. For example, the scenario generator module 34 may create aseries of scenarios which will then be sent to the system analysis andcontrol module 28 to analyze and validate; the system analysis andcontrol module 28 may ask the system coordinators 12, 14, 16, 18, 20, 22to make predictions on the scenarios generated by the scenario generatormodule 34. Once the associated system coordinator predictions arereceived and validated, the system analysis and control module 28 willestablish which combinations of scenarios may result in the lowestenergy consumption or energy cost level. The system analysis and controlmodule 28 will then send these combinations to the machine learningmodule 30 to make predictions, as previously described, or it may sendthem back to the scenario generator module 34 for analysis. Afteranalyzing the predictions made by the machine learning module 30 or thesystem coordinator combination scenarios received from the systemanalysis and control module 28, the scenario generator module 34 maydecide to direct the system analysis and control module 28 to implementa specific combination of system coordinator scenarios. The systemanalysis and control module 28 will then direct the system coordinators12, 14, 16, 18, 20, 22 to execute the scenarios associated with thatspecific combination.

The scenario generator module 34 may create scenarios by modelling zoneagents under different conditions (e.g. various zone temperaturesetpoints, various supply airflow setpoints and associated temperature,various lighting loads, various plug loads, etc.), by modelling AHUs asdelivering various airflows at various temperatures, by modelling thechilled water plant as delivering various chilled water temperatures andvarious associated chilled water flows, by modelling the condenser waterplant as delivering various condenser water temperatures and condenserwater flows, or by modeling the hot water plant as delivering varioushot water temperatures and associated water flows, etc.

B. Zone Agent

1. General Description

The zone agent is an independent, multifunctional software agentresponsible for management of zones throughout the building. A “zone”can be comprised of one or more rooms, one or more lighting controlzones, one or more receptacle control zones, and one or moreheating/cooling terminal units. The functions and responsibilities ofthe zone agent include but are not limited to:

-   -   Prediction of current and future heating and cooling loads.    -   Measurement of current occupancy and prediction of future        occupancy.    -   Prediction of terminal unit reheat coil performance under        various combinations of entering air temperature, entering water        temperature, etc.    -   Prediction of terminal unit cooling coil performance under        various combinations of entering air temperature, entering water        temperature, etc.    -   Prediction of terminal unit energy consumption under various        combinations of entering air temperature, entering water        temperatures, etc.    -   Modulation of terminal unit actuators to satisfy room heating        and cooling as directed by SMITHGROUP-AI 10.    -   Zone lighting control.    -   Zone receptacle control for switched receptacle circuits.    -   Monitoring of total zone power consumption.    -   Monitoring of zone receptacle power consumption and prediction        of future zone receptacle power consumption.    -   Operation of motorized shading.    -   Airflow/ventilation optimization.

2. Internal Structure

Referring to FIG. 3, the internal structure of the zone agent 36 and itsrelated environment is shown. The environment for the zone agent 36 iscomprised of the sensors 38, 40, 42, 44 within the zone, global sensors,weather data from an internet source (API) 46, 48, and effectors 50, 52,54, 56 within the zone. The agent 36 is comprised of four modules, eachwith its own dedicated algorithms and controls logic.

The system feedback module 58 is responsible for the following:

-   -   Collection of information from all associated sensors 38, 40,        42, 44, data sources 46, 48, and effectors 50, 52, 54, 56; the        data received will be compared against the predictions made by        the machine learning module 60. Where appropriate, a time delay        will be implemented when comparing the measured data to        predictions; data will also be compared to historical data to        allow for the filtering of outlier data that may be caused by a        malfunctioning sensor or atypical temporary conditions. Once the        implementation of appropriate time delays and filtering is        complete, the system feedback module 58 will then send the        measured values to the machine learning module 60 such that the        machine learning module 60 can update its internal learning        algorithms.

The machine learning module 60 is responsible for the following:

-   -   Collection of information from the system feedback module 58.    -   Making predictions using machine learning algorithms.    -   Sending predictions to the system analysis and control module        62, and associated higher level coordinators where applicable.

The machine learning module 60 will contain numerous machine learningalgorithms, including, but not limited to the following.

-   -   1. Current sensible heating/cooling load prediction algorithm.        This algorithm will predict the current sensible heating or        cooling load in Btu/hr for the zone. Data inputs used may        include, but not be limited to the following: zone temperature        setpoint, zone temperature, zone relative humidity setpoint,        zone relative humidity, daylighting, total lighting heat output,        outdoor air temperature, outdoor air dew point, outdoor air wind        speed, outdoor air wind direction, current motorized shade        position, current number of occupants, and current plug load.        Initially, a data set produced using a load simulation software        will be used to train a machine learning algorithm. This will be        referred to as a “pre-trained algorithm.” Upon implementation,        the agent 36 will build a new data set using real operational        data to train a new machine learning algorithm which will be        referred to as an “operational algorithm.” The feedback used to        train the operational algorithm will be zone temperature drift        after a specified period. The agent 36 will continue to use the        pre-trained algorithm until the operational algorithm has        achieved a specified level of accuracy—at which point the        transition will occur. If the operational algorithm data should        become inaccurate, the agent 36 will revert to the pre-trained        algorithm. For example, if SMITHGROUP-AI 10 is implemented        during a period of warm weather, the operational algorithm may        become accurate enough to take over from the pre-trained        algorithm within one to two months. However, the temperature may        drop after several months at which point the algorithm may        become inaccurate due to not having accrued enough training data        for periods of cold weather. Should this occur, the agent 36 may        revert to using the pre-trained algorithm, until such a time        that the operational algorithm has built up enough training data        and achieved the specified level of accuracy. Note that the data        sets for the pre-trained algorithm, and operational algorithm        will never be mixed—this transition type will be referred to as        a “hand-off transition.”    -   2. Current latent dehumidification/humidification load        prediction algorithm: this algorithm will predict the current        latent dehumidification/humidification load in Btu/hr for the        zone. Data inputs used may include, but not be limited to the        following: zone relative humidity setpoint, zone relative        humidity, outdoor air dew point, outdoor air wind speed, outdoor        air wind direction, current number of occupants, and current        plug load. Initially, a data set produced using load simulation        software will be used to train a machine learning algorithm.        This will be referred to as a “pre-trained algorithm.” Upon        implementation, the agent 36 will build a new data set using        real operational data to train a new machine learning algorithm        that will be referred to as the “operational algorithm.” The        feedback used to train the operational algorithm will be zone        relative humidity drift after a period of time. The transition        from the pre-trained algorithm to the operational algorithm        shall be a hand-off transition. Refer to machine learning        algorithm one for additional information.    -   3. Future sensible heating/cooling load prediction algorithm.        This algorithm will predict future sensible heating/cooling load        in Btu/hr for the zone. Data inputs used may include, but not be        limited to the following: predicted zone temperature setpoint,        predicted number of occupants, predicted total lighting heat        output, predicted plug load, future outdoor air temperature (via        API), future outdoor air wind speed (via API), future outdoor        air wind direction (via API), future solar azimuth (via API),        future solar altitude (via API), future cloud cover (via API),        and predicted motorized shade position. Initially, a data set        produced using load simulation software will be used to train a        machine learning algorithm. This will be referred to as a        “pre-trained algorithm.” Upon implementation, the agent 36 will        build a new data set using real operational data to train a new        machine learning algorithm which will be referred to as the        “operational algorithm.” The feedback used to train the        operational algorithm will be the actual cooling load. This        process represents an indirect feedback loop in which each        prediction the algorithm makes will not necessarily receive        feedback. For example, if the operational algorithm predicts        that a zone cooling load will be 10,000 Btu/hr in five hours        based on the forecasted weather data in which total cloud cover        is expected, but in five hours' time, the cloud cover is        minimal, the prediction will be inaccurate. However, the cause        of the inaccuracy was the uncertainty of the weather forecast        rather than an issue with the algorithm. For this reason, the        algorithm will monitor real time cooling load and the data        inputs described above that occur simultaneous to the real time        cooling load to build the data set. The transition from the        pre-trained algorithm to the operational algorithm will be a        hand-off transition. Refer to machine learning algorithm one for        additional information.    -   4. Future latent dehumidification/humidification load prediction        algorithm. This algorithm will predict the future latent        dehumidification/humidification load in Btu/hr for the zone.        Data inputs used may include, but not be limited to the        following: predicted zone relative humidity setpoint, predicted        number of occupants, future outdoor air dew point (via API),        future outdoor air wind speed (via API), future outdoor air wind        direction, and predicted plug load. Initially, a data set        produced using load simulation software will be used to train a        machine learning algorithm. This will be referred to as a        “pre-trained algorithm.” Upon implementation, the agent 36 will        build a new data set using real operational data to train a new        machine learning algorithm that will be referred to as the        “operational algorithm.” The feedback used to train the        operational algorithm will be the actual latent        dehumidification/humidification load. This process represents an        indirect feedback loop. Refer to machine learning algorithm        three for additional information on feedback. The transition        from pre-trained algorithm to the operational algorithm will be        a hand-off transition. Refer to machine learning algorithm one        for additional information.    -   5. Future temperature setpoint prediction algorithm. This        algorithm will predict the future temperature setpoint for any        given time. Data inputs used may include, but not be limited to        the following: day of the week, day type (holiday/non-holiday),        and time. Initially, a setpoint schedule will be produced to        train a machine learning algorithm. This will be referred to as        a “pre-trained algorithm.” Upon implementation, the agent 36        will build a new data set using real operational data to train a        new machine learning algorithm that will be referred to as the        “operational algorithm.” The feedback used to train the        operational algorithm will be actual setpoint data. A complete        transition from the pre-trained algorithm to the operational        algorithm will take place after a specified period. Both the        pre-trained and operational algorithm may be overridden by user        intervention or in coordination with a room scheduling platform.    -   6. Future relative humidity setpoint prediction algorithm. This        algorithm will predict the future relative humidity setpoint for        any given time. Data inputs used may include, but not be limited        to the following: day of the week, day type (e.g.        holiday/non-holiday), and time. Initially, a setpoint schedule        will be produced to train a machine learning algorithm. This        will be referred to as a “pre-trained algorithm.” Upon        implementation, the agent will build a new data set using real        operational data to train a new machine learning algorithm that        will be referred to as the “operational algorithm.” The feedback        used to train the operational algorithm will be actual setpoint        data. A complete transition from the pre-trained algorithm to        the operational algorithm will take place after a specified        period. Both the pre-trained and operational algorithm may be        overridden by user intervention or in coordination with a room        scheduling platform.    -   7. Predicted number of occupants algorithm. This algorithm will        predict the future number of occupants for any given time. Data        inputs used may include, but not be limited to the following:        day of the week, day type (e.g. holiday/non-holiday), and time.        Initially, a setpoint schedule will be produced to train a        machine learning algorithm. This will be referred to as a        “pre-trained algorithm.” Upon implementation, the agent 36 will        build a new data set using real operational data to train a new        machine learning algorithm that will be referred to as the        “operational algorithm.” The feedback used to train the        operational algorithm will be actual number of occupants as        sensed by a people counter or as calculated using room CO₂        levels. A complete transition from the pre-trained algorithm to        the operational algorithm will take place after a specified        period. Both the pre-trained and operational algorithm may be        overridden by user intervention or in coordination with a room        scheduling platform.    -   8. Predicted plug load. This algorithm will predict the future        plug load for any given time. Data inputs used may include, but        not be limited to the following: day of the week, day type (e.g.        holiday/non-holiday), predicted number of occupants, and time.        Initially, a setpoint schedule will be produced to train a        machine learning algorithm. This will be referred to as a        “pre-trained algorithm.” Upon implementation, the agent 36 will        build a new data set using real operational data to train a new        machine learning algorithm that will be referred to as the        “operational algorithm.” The feedback used to train the        operational algorithm will be the actual plug load as sensed by        the meter on the associated receptacle circuit. The transition        from the pre-trained algorithm to the operational algorithm will        be a hand-off transition. Refer to machine learning algorithm        one for additional information.    -   9. Future perimeter zone lighting control signal. This algorithm        will predict the future lighting control signal for the exterior        lighting control zone within a zone for any given time. Data        inputs used may include, but not be limited to the following:        predicted number of occupants, future solar azimuth, future        solar altitude, future cloud cover, and future motorized shade        position. Each lighting circuit will be sub metered. Upon        building start up, the scenario generator module 64 in        combination with the system analysis and control module 62 will        turn off all but one lighting zone at a time and modulate from        minimum to maximum a lighting control signal while the system        feedback module 58 records the metered wattage associated with        each lighting control signal. Therefore, the predicted perimeter        zone lighting control signal can be used to predict exterior        zone lighting heat output in Btu/hr. The process described above        also applies to the middle, and interior lighting control zones.    -   10. Future middle zone lighting control signal. This algorithm        will predict the future lighting control signal for the middle        lighting control zone within a zone for any given time. Refer to        machine learning algorithm nine for additional information.    -   11. Future interior zone lighting control signal. This algorithm        will predict the future lighting control signal for the interior        lighting control zone within a zone for any given time. Refer to        machine learning algorithm nine for additional information.    -   12. Future total lighting heat output: this algorithm will        predict the total lighting heat output for any given time. Data        inputs may include but not be limited to the following: future        perimeter zone lighting control signal, future middle zone        lighting control signal, interior zone lighting control signal.        Initially, a data set produced using load simulation software        will be produced to train a machine learning algorithm. This        will be referred to as a “pre-trained algorithm.” Upon        implementation, the agent 36 will build a new data set using        real operational data to train a new machine learning algorithm        that will be referred to as the “operational algorithm.” The        feedback used to train the operational algorithm will be the        actual lighting control signals. This process represents an        indirect feedback loop. Refer to machine learning algorithm        three for additional information on feedback. The transition        from the pre-trained algorithm to the operational algorithm will        be a hand-off transition. Refer to machine learning algorithm        one for additional information.    -   13. Predicted change in temperature rate. This algorithm will        predict the rate at which the temperature in the zone will        change. Data inputs used may include, but not be limited to        airflow (CFM), and a differential between predicted zone load        (Btu/hr) and the heating/cooling provided to the zone (Btu/hr).        Initially, a data set produced using mathematical formulas and        the volume of the space will be produced to train a machine        learning algorithm. This will be referred to as a “pre-trained        algorithm.” Upon implementation, the agent will build a new data        set using real operational data to train a new machine learning        algorithm that will be referred to as the “operational        algorithm.” The feedback used to train the operational algorithm        will be the actual change in temperature rate. The scenario        generator module 64, in combination with the system analysis and        control module 62 will be responsible for changing the airflow        and Btu/hr offset to the space during unoccupied periods to        build the training set for the operational algorithm. The        transition from the pre-trained algorithm to the operational        algorithm will be a hand-off transition. Refer to machine        learning algorithm one for additional information.    -   14. Predicted change in relative humidity rate. This algorithm        will predict the rate at which the relative humidity in the zone        will change. Data inputs used may include, but not be limited to        airflow (CFM), and a differential between predicted zone load        (Btu/hr) and the dehumidification/humidification provided to the        zone (Btu/hr). Initially, a data set produced using mathematical        formulas and the volume of the space will be produced to train a        machine learning algorithm. This will be referred to as a        “pre-trained algorithm.” Upon implementation, the agent will        build a new data set using real operational data to train a new        machine learning algorithm that will be referred to as the        “operational algorithm.” The feedback used to train the        operational algorithm will be the actual change in relative        humidity rate. The scenario generator module 64, in combination        with the system analysis and control module 62 will be        responsible for changing the airflow and Btu/hr offset to the        space during unoccupied periods to build the training set for        the operational algorithm. The transition from pre-trained        algorithm to the operational algorithm will be a hand-off        transition. Refer to machine learning algorithm one for        additional information.    -   15. Hydronic coil characteristics. This algorithm will predict        the leaving water temperature, and flow rate. Data inputs used        may include, but not be limited to airflow, entering water        temperature, entering air temperature, and leaving air        temperature. Initially, a data set produced by mapping coil        characteristics using manufacturer's data will be used to train        a machine learning algorithm. This will be referred to as a        “pre-trained algorithm.” Upon implementation, the agent will        build a new data set using real operational data to train a new        machine learning algorithm that will be referred to as the        “operational algorithm.” The feedback used to train the        operational algorithm will be the actual leaving water        temperature and flow rate. The transition from the pre-trained        algorithm to the operational algorithm will be a hand-off        transition. Refer to machine learning algorithm one for        additional information.    -   16. Energy consumption. This algorithm will predict the terminal        unit energy consumption for various operating scenarios. Data        inputs used may include, but not be limited to the following:        primary airflow (CFM) and fan airflow (CFM). Initially, a data        set produced using manufacturer's data will be used to train a        machine learning algorithm. This will be referred to as a        “pre-trained algorithm.” Upon implementation, the agent will        build a new data set using real operational data to train a new        machine learning algorithm that will be referred to as the        “operational algorithm.” The feedback used to train the        operational algorithm will be the actual terminal unit energy        consumption. The transition from the pre-trained algorithm to        the operational algorithm will be a hand-off transition. Refer        to machine learning algorithm one for additional information.    -   17. Airflow algorithm. This algorithm will determine the amount        of airflow the zone will receive for each potential entering air        temperature. Data inputs used may include, but not be limited to        the following: zone heating/cooling load, zone        humidification/dehumidification load, entering air temperature,        entering air dew point, number of occupants, zone CO₂        concentration, zone VOC concentration, zone airborne particulate        concentration, and air handling unit system ventilation        efficiency. The algorithm will produce a single airflow value        for each potential entering air temperature. Initially, a data        set produced using load simulation software will be used to        train a machine learning algorithm. This will be referred to as        a “pre-trained algorithm.” Upon implementation, the agent 36        will build a new data set using real operational data to train a        new machine learning algorithm that will be referred to as the        “operational algorithm.” The feedback used to train the        operational algorithm will be zone relative humidity drift after        a period. The transition from the pre-trained algorithm to the        operational algorithm shall be a hand-off transition. Refer to        machine learning algorithm one for additional information. The        algorithm will be constrained by high and low limits—referred to        as laws which can be reset through user intervention or by        direction from SMITHGROUP-AI 10, the scenario generator module        64, and the system analysis and control module 62.    -   18. Predicted zone receptacle power consumption. This algorithm        will predict the total zone receptacle power consumption for any        given time. The purpose of this algorithm is to predict the        energy consumption of various equipment such as kitchen        appliances, computers, data servers, etc. Data inputs used may        include, but not be limited to the following: number of        occupants, time, day of week, and day type (e.g. holiday or        non-holiday). Initially, a data set produced using load        simulation software will be used to train a machine learning        algorithm. This will be referred to as a “pre-trained        algorithm.” Upon implementation, the agent 36 will build a new        data set using real operational data to train a new machine        learning algorithm that will be referred to as the “operational        algorithm.” The feedback used to train the operational algorithm        will be actual zone receptacle power consumption as measured by        the meter on the associated receptacle circuit. The transition        from the pre-trained algorithm to the operational algorithm        shall be a hand-off transition. Refer to machine learning        algorithm one for additional information.        Various algorithms described above can be limited to a specific        range using lower, and higher limit laws.

The machine learning algorithm outputs will form a data set of potentialoperating scenarios which will be shared with the AHU systemcoordinators 20, 22, chilled water system coordinator 16, condenserwater system coordinator 12, heating hot water system coordinator 18,and power system coordinator 14, where applicable. For example, if azone agent 36 is responsible for the control of a chilled water fan coilunit, the zone agent 36 will send data sets to the appropriate one ofthe AHU system coordinators 20, 22, and chilled water system coordinator16; if the zone agent 36 is responsible for the control of a VAV boxwith a heating hot water reheat coil, the zone agent 36 will send datato the appropriate one of the AHU system coordinators 20, 22 and theheating hot water system coordinator 18.

In addition to the algorithms described above, zones which featurefrequent dry bulb temperature/dew point temperature setpoint changeswill include the following algorithms. The algorithms below can be usedin combination with scheduled/predicted future setpoints.

-   -   19. Temperature setpoint change. this algorithm will predict the        amount of time required to heat or cool a zone between two        temperature setpoints. Data inputs may include, but not be        limited to the following: initial setpoint, final setpoint,        airflow (CFM), current sensible heating/cooling load, future        sensible heating/cooling load, number of occupants, and        discharge air temperature. Initially, a data set produced using        mathematical formulas (e.g. HVAC formulas, differential        equations etc.) and the volume of the room will be used to train        a machine learning algorithm. This will be referred to as a        “pre-trained algorithm.” Upon implementation, the agent 36 will        build a new data set using real operational data to train a new        machine learning algorithm which will be referred to as the        “operational algorithm.” The feedback used to train the        operational algorithm will be the actual amount of time required        to heat or cool the zone from the initial or final temperature.        The scenario generator module 64, in combination with the system        analysis and control module 62 will be responsible for running        test scenarios where the temperature setpoint is changed during        unoccupied periods. The tests will be conducted using various        airflows and discharge air temperatures to build the operational        algorithm's data set. Once the operational algorithm is        implemented, the scenario generator module 64 in combination        with SMITHGROUP-AI 10 and the system analysis and control module        62 will calculate various scenarios for heating/cooling the room        from the initial to final setpoint and determine which method is        most economical. For example, consider a room scheduled or        predicted to be occupied from 8:00 am to 9:30 am with a setpoint        of 80° F., scheduled or predicted to be unoccupied from 9:30 am        to 10:00 am, and scheduled or predicted to be occupied from        10:00 am to 11:00 am with a setpoint of 60° F. Therefore, the        system will have 30 minutes to cool the room from 80° F. to        60° F. The scenario generator module 64 in combination with        SMITHGROUP-AI 10 and the system analysis and control module 62        will evaluate whether it is more economical to cool the room        slowly over a longer period of time (up to 30 minutes) or        quickly over a short period of time—and when that short period        of time should be—for example, if the cloud cover is expected to        clear at 9:45 am—thus increasing the cooling loads and energy        associated with meeting the loads—then it may be more energy or        energy cost efficient to cool the room quickly between 9:30 am        and 9:45 am, then maintain the temperature for the following 15        minutes. The transition from the pre-trained algorithm to the        operational algorithm shall be a hand-off transition. Refer to        machine learning algorithm one for additional information. The        strategies described above also apply to machine learning        algorithm twenty.    -   20. Dew point temperature setpoint change. This algorithm will        predict the amount of time required to heat or cool a zone        between two dew point temperature setpoints. Note that dew point        is used as opposed to relative humidity as the dew point        setpoint change will often coincide with a dry bulb temperature        change. Data inputs may include, but not be limited to the        following: initial setpoint, final setpoint, airflow (CFM),        current latent dehumidification/humidification load, future        latent dehumidification/humidification load, number of        occupants, and entering air dew point temperature. Initially, a        data set produced using mathematical formulas and the volume of        the room will be used to train a machine learning algorithm.        This will be referred to as a “pre-trained algorithm.” Upon        implementation, the agent 36 will build a new data set using        real operational data to train a new machine learning algorithm        which will be referred to as the “operational algorithm.” The        feedback used to train the operational algorithm will be the        actual amount of time required to humidity or dehumidify the        zone from the initial or final dew point temperature. Refer to        machine learning algorithm nineteen for additional information.        The transition from the pre-trained algorithm to the operational        algorithm shall be a hand-off transition. Refer to machine        learning algorithm one for additional information.

The system analysis and control module 62 is responsible for thefollowing:

-   -   Monitoring and control of all sensors 38, 40, 42, 44 and        actuators 50, 52, 54, 56 related to the zone agent 36.    -   Analyzing the data from each sensor and actuator. For example,        the system analysis and control module 62 may determine that due        to a flex duct being kinked, higher end airflow values can no        longer be provided, and update the high limit airflow law for        the airflow algorithm.    -   Sending the valid system scenarios to the machine learning        module 60 for predictions.    -   Sending commands to the various zone effectors 50, 52, 54, 56.        The commands could be valve position, lighting control signal,        damper position, or shade position.    -   Analyzing the scenarios proposed by the scenario generator        module 64. For example, if SMITHGROUP-AI 10 determines that the        zone in question is considered critical (e.g. is the zone        preventing the HVAC system from operating at a more energy        efficient condition), then the scenario generator 64 shall        create scenarios to be analyzed by the system analysis and        control module 62 such as relaxing the temperature or relative        humidity set point or lowering the motorized shades. The system        analysis and control module 62 shall evaluate the created        options and determine if they are acceptable based on the        current zone occupant number, and the zone importance factor: a        numerical value between 0-10 indicating how important space        temperature and lighting levels are. For zones with high        importance factors such as main conference rooms etc., the        system analysis and control module 62 may determine that certain        scenarios such as relaxing temperature setpoints are not        acceptable. If determined to be acceptable, the system analysis        and control module 62 will send the scenario to the machine        learning module 60 for predictions.

The scenario generator module 64 is responsible for receiving data fromSMITHGROUP-AI 10, via its associated system coordinator, and generatingnew operating scenarios for the zone in response. For example,SMITHGROUP-AI 10 may determine that the zone is the most critical from aventilation standpoint. In response, the scenario generator module 64may request that the system analysis and control module 62 raise theairflow algorithm minimum airflow law to provide more airflow to thezone.

3. Sample Process

Refer to AHU system, chilled water system, and heating hot water systemfor examples of the data sets produced by the zone agent 36 and how theyare used.

C. Power and Lighting Systems

1. General Description

Considering a power monitoring and controls system, the electricityconsumption of each zone circuit within the lighting panelboard andwithin the power panelboard is monitored via a dedicated meter. Further,the power for each zone circuit within the lighting panelboard andwithin the power panelboard may be turned on and off via the dedicatedcircuit breaker. All sensors and actuators are connected directly to thenetwork, without the use of proprietary controllers that operate withprogrammed sequences of operation. In some instances, an open sourcenon-proprietary input/output module or a gateway may be required toconvert the signal from a sensor or an actuator such that it can becommunicated via open source networks such as BACnet, LONworks, Modbus,etc.

Considering a renewable energy power monitoring and controls system, thecontrols of the wind turbines and of the solar panels are done throughthe manufacturer provided proprietary control panels. The control panelsare connected to the network through integration via open sourcenon-proprietary input/output modules or gateways. In some instances, thesensors and actuator associated with the wind turbine controls systemsand solar panel controls systems may be connected directly to thenetwork thru non-proprietary input/output modules or gateways.

The control of the entire power system is performed through a series ofindependent software agents such as the power and lighting systemcoordinator 66, lighting and power panelboard agents 68, 70, utilityagent 72, renewable energy agent 74, and zone agents 36′, 36″. Thecommunication architecture between the various agents and coordinatorsis shown in FIG. 4.

2. Power and Lighting System Coordinator

a. Purpose

The power and lighting system coordinator 66 is an independent softwareagent that monitors and controls all agents associated with the powerand lighting control systems. Further, the power and lighting systemcoordinator 66 is responsible for the following:

-   -   Prediction of the energy use of all building systems.    -   Prediction of the energy generation from the renewable systems.    -   Communication with SMITHGROUP-AI 10: sends overall power use and        power available predictions and receives commands from        SMITHGROUP-AI 10.    -   Communication with the panelboard agents 68, 70: requests power        use predictions and status from the panel agents 68, 70.    -   Communication with the renewable energy agent 74: request power        available predictions and status from the renewable energy agent        74.    -   Communication with the zone agents 36′, 36″: receives commands        and feedback from the zone agents 36′, 36″.    -   Communication with the utility agent 72: receives utility status        (e.g. loss of power).

b. Internal Structure

Referring to FIG. 5, the internal structure of the power and lightingsystem coordinator 66 and its related environment is shown. Theenvironment for the lighting and power system coordinator 66 iscomprised of all the agents that it monitors and controls. The agent iscomprised of five modules, each with its own dedicated algorithms andcontrols logic.

The data filtering module 76 is responsible for separating the datareceived from the various agents 36′, 36″, 68, 70, 72, 74. For example,the actual agent power consumption levels will be sent to the systemfeedback module 78, while predictions from the agents 36′, 36″, 68, 70,72, 74 will be sent to the system analysis and control module 80.

The system feedback module 78 is responsible for the following:

-   -   Collection of information from the zone agents 36′, 36″,        panelboard agents 68, 70, utility agent 72, and renewable energy        agent 74; the information may represent status (e.g. failed,        maintenance) or predictions related to the energy that may be        generated by the renewable energy agent 74. A failed status may        indicate that an agent may no longer control its environment and        should be excluded from the overall power system predictions        that the machine learning module 82 is making. A maintenance        status may indicate that an agent has entered or may enter into        a maintenance mode and that the machine learning module 82 needs        to update its overall power system predictions accordingly.    -   Analysis of predictions related to the total power system        consumption. The actual meter data from the various agents 36′,        36″, 68, 70, 72, 74 will be compared against their own        predictions and against the predictions made by the machine        learning module 82. Any large discrepancies between the        predicted and actual power usage could mean that the previous        command that one of the zone agents 36′, 36″ or the renewable        energy agent 74 received from the power coordinator 66 has        placed one of the zone agents 36′, 36″ or the renewable energy        agent 74 in conflict with its own internal laws (e.g. not able        to meet the various power requirements). This could indicate        that the difference in the error or accuracy of the predictions        made by the machine learning module 82 and the actual outcome        might have exceeded critical limits, which in turn has affected        the overall performance of the renewable energy system. The        system feedback module 78 will then send the actual meter data        to the machine learning module 82 such that the machine learning        module 82 can update its internal learning algorithms.    -   Collection of information from SMITHGROUP-AI 10. The information        may represent commands that need to be distributed to the        various associated agents 36′, 36″, 68, 70, 72, 74. The        information may also represent feedback regarding the        predictions that the lighting and power system coordinator 66        has made; for example, the predicted energy performance of the        scenario that SMITHGROUP-AI 10 has directed the lighting and        power system coordinator 66 to implement is significantly        different that the real outcome. As such the system feedback        module 78 will pass this feedback to the machine learning module        82 to update its machine learning algorithms accordingly such        that the accuracy or error of its predictions self-improve.    -   Collection of information from the utility agent 72. The        information may represent status of the available utility power.        For example, should the building experience a loss of power, the        lighting and power system coordinator 66 will need to notify        SMITHGROUP-AI 10 of such condition. SMITHGROUP-AI 10 may then        direct all system coordinators to operate under an emergency        power mode until the utility agent 72 notifies that utility        power is available.

The machine learning module 82 is responsible for the following:

-   -   Collection of information from the system feedback module 78.        The type of information that this module will collect is        described under the system feedback module 78.    -   Collection of information from the system analysis and control        module 80. This information may represent predictions from the        various agents 36′, 36″, 68, 70, 72, 74 regarding available        power or used power.    -   Making predictions, using machine learning algorithms, based on        the data received from the system analysis and control module        80, for the overall energy consumption of the entire system or        for the total energy that the renewable power agent 74 may need        to deliver or can deliver.    -   Sending the predictions to the system analysis and control        module 80 and to the scenario generator module 84.

The system analysis and control 80 module is responsible for thefollowing:

-   -   Monitoring and control of all the agents 36′, 36″, 68, 70, 72,        74 associated with the power and lighting system coordinator 66.    -   Collection of information from the data filtering module 76. The        type of information that this module will collect is described        under the data filtering module 76.    -   Analyzing the data from each agent, and filtering and compiling        the data into valid system scenarios.    -   Sending the valid system scenarios to the machine learning        module 82 for predictions.    -   Receiving the predictions from the machine learning module 82        and sending commands to the various agents 36′, 36″, 68, 70, 72,        74. The commands that the system analysis and control module 80        may send to the agents 36′, 36″, 68, 70, 72, 74 could be battery        storage setpoints, energy use setpoints, requests for        predictions, or switching on and off various circuits within the        lighting and power panelboards 68, 70.    -   Analyzing the scenarios proposed by the scenario generator        module 84. For each scenario received, the system analysis and        control module 80 may decide to direct the various agents 36′,        36″, 68, 70, 72, 74 to make predictions and then compile these        predictions into valid system scenarios for the machine learning        module 82 to make predictions on.

The scenario generator module 84 is responsible for continuously lookingfor ways to improve the overall energy performance of the entire powerdistribution system. For example, the scenario generator module 84 maycreate a series of scenarios which will then be sent to the systemanalysis and control module 80 to analyze and validate; the systemanalysis and control module 80 may ask the agents 36′, 36″, 68, 70, 72,74 to make predictions on the scenarios. Once the scenarios arevalidated, they may be sent to the machine learning module 82 to makepredictions on. The predictions made by the machine learning module 82will then be sent back to the scenario generator module 84 for analysis.After analyzing the predictions, the scenario generator module 84 maydecide to send such predictions to SMITHGROUP-AI 10, which in turn maydirect the power and lighting system coordinator 66 to implement one ofthe scenarios created by the scenario generator module 84.

The scenario generator module 84 may create scenarios by modelling therenewable energy agent 74 as delivering various power and by modellingthe zone agents 36′, 36″ as satisfying their zone power conditions undervarious conditions.

3. Panelboard Agents

a. Purpose

A panelboard agent is an independent software agent that monitor andcontrols all sensors and actuators associated with a panelboard (e.g.lighting panelboard, power panelboard etc.). Each panelboard within thepower distribution system is monitored and controlled by a dedicatedpanelboard agent 68, 70.

The panelboard agents 68, 70 are responsible for the following:

-   -   Prediction of the energy use of each zone. The panelboard agents        68, 70 will communicate, via the power and lighting system        coordinator 66, with each zone such that its predictions are        coordinated and validated based on the specific zone data and        predictions.    -   Prediction of the energy use of the entire panelboard.    -   Communication with the power and lighting system coordinator 66.        Sends panelboard energy use predictions (for the entire        panelboard and for each zone) and receives commands from the        power and lighting system coordinator 66.

b. Internal Structure

Referring to FIG. 6, the internal structure of a panelboard agent 68 andits related environment is shown. The environment for the panelboardagent 68 is comprised of all the sensors 86, 88, 90, 92, 94, 96 andactuators 98, 100, 102, 104 that are located within a panelboard. Insome instances, an open source non-proprietary input/output module or agateway may be required to convert the signal from a sensor or anactuator such that it can be communicated via open source networks suchas BACnet, LONworks, Modbus etc. The agent 68 is comprised of fivemodules, each with its own dedicated algorithms and controls logic.

The data filtering module 106 is responsible for separating the datareceived from sensors 86, 88, 90, 92, 94, 96 and actuators 98, 100, 102,104. For example, the actual energy consumption levels of each circuitmay be sent to the system feedback module 108, while data (e.g. sensoror actuator status, etc.) will be sent to the system analysis andcontrol module 110. Further, zone data (e.g. predictions) received fromthe power and lighting system coordinator 66 may be sent to the systemanalysis and control module 110. The data filtering module 106 may alsosend to the system analysis and control module 110 the same data thatwas sent to the system feedback module 108. A sensor within thepanelboard may represent an electricity meter or a status signal from acircuit breaker. An actuator within the panelboard may represent acircuit breaker that can be commanded on or off.

The system feedback module 108 is responsible for the following:

-   -   Collection of information from the various sensors 86, 88, 90,        92, 94, 96 and actuators 98, 100, 102, 104. The type of        information that the system feedback module 108 may collect is        described in the data filtering module 106.    -   Analysis of predictions related to the energy level consumption        levels for each zone. The data received from the various sensors        86, 88, 90, 92, 94, 96 and actuators 98, 100, 102, 104 will be        compared against the predictions made by the machine learning        module 112. Any large discrepancies between the predicted and        measured values could mean that the previous commands could        indicate that the difference in the error or accuracy of the        predictions made by the machine learning module 112 and the        actual outcome might have exceeded critical limits, which in        turn has affected the performance of the power system. The        system feedback module 108 will then send the measured values to        the machine learning module 112 such that the machine learning        module 112 can update its internal learning algorithms.    -   Collection of information from the power and lighting system        coordinator 66. The information may represent feedback regarding        the predictions that the panelboard agent 68 has made.

The machine learning module 112 is responsible for the following:

-   -   Collection of information from the system feedback module 108.        The type of information that this module will collect is        described under the system feedback module 108.    -   Collection of information from the system analysis and control        module 110. This information may represent data from the various        sensors 86, 88, 90, 92, 94, 96 and actuators 98, 100, 102, 104,        such as airflows and associated temperatures.    -   Making predictions, using machine learning algorithms, based on        the data received from the system analysis and control module        110 or from the system feedback module 108.    -   Sending the predictions to the system analysis and control        module 110 and to the scenario generator module 114.

The system analysis and control module 110 is responsible for thefollowing:

-   -   Monitoring and control of all sensors 86, 88, 90, 92, 94, 96 and        actuators 98, 100, 102, 104 related to the panelboard. control        of actuators 98, 100, 102, 104 may represent switching on and        off a circuit breaker, and thus power, associated with a        specific zone agent. For example, a zone agent may predict that        the power to the “switched” receptacles can be turned off at a        specific time. The zone agent may communicate this event to the        power and lighting system coordinator 66, which in turn may        direct the panelboard agent 68 to switch off the associated        circuit breaker.    -   Collection of information from the data filtering module 106.        The type of information that this module will collect is        described under the data filtering module 106.    -   Analyzing the data from each sensor and actuator. For example,        the system analysis and control module 110 may determine that a        zone is consuming significantly more energy than what was        predicted (by both the machine learning module of the zone agent        and by the machine learning module 112 of the panelboard agent        68). As such, the system analysis and control module 110 may ask        that specific zone agent, via the power and lighting system        coordinator 66, to verify the accuracy of its sensors and        predictions.    -   Analyzing the scenarios proposed by the scenario generator        module 114. For each scenario received, the system analysis and        control module 110 will analyze the status of the sensors 86,        88, 90, 92, 94, 96 and actuators 98, 100, 102, 104 and will        determine which scenario is valid. For example, one of the        scenarios that the scenario generator module 114 may generate        will require some of the zones to consume 15% less energy than        usual. the system analysis and control module 110 may determine,        after communication with the associated zone agents (via the        power and lighting system coordinator 66), that such scenario is        no longer valid and that the machine learning module 112 will        not make a prediction on the referenced scenario.

The scenario generator module 114 is responsible for continuouslylooking for ways/scenarios to improve the overall energy performance ofthe power systems associated with it. For example, the scenariogenerator module 114 may create a series of scenarios that will then besent to the system analysis and control module 110 to analyze andvalidate. Once the scenarios are validated, they may be sent to thevarious zone agents (for analysis and predictions), via the power andlighting system coordinator 66, or to the machine learning module 112 tomake its own predictions. The predictions made by the machine learningmodule 112 will then be sent back to the scenario generator module 114for analysis. After analyzing the predictions, the scenario generatormodule 114 may decide to send such predictions to the power and lightingsystem coordinator 66, which may send them to SMITHGROUP-AI 10, which inturn may direct the power and lighting system coordinator 66 toimplement one of the scenarios created by the scenario generator module66.

The scenario generator module 114 may create scenarios by turning on andoff various receptacle, lighting, and equipment circuits at a certaintime. Each such scenario will have an impact on the energy performanceof the power system and on the heating and cooling loads within a zone.

4. Renewable Energy Agent

a. Purpose

The renewable energy agent 74 is an independent software agent thatmonitors and controls all renewable energy systems connected to thepower distribution system. The control of wind turbines and solarpanels, for example, is done through the manufacturer providedproprietary control panels. The control panels are connected to thenetwork thru integration via open source non-proprietary input/outputmodules or gateways. In some instances, the sensors and actuatorassociated with the wind turbine control systems and solar panel controlsystems may be connected directly to the network through non-proprietaryinput/output modules or gateways.

The sensors that the renewable energy agent 74 may monitor are batterylevels, status of solar panels, status of windmills, weather data, etc.The actuators that the renewable energy agent 74 may control are turningon/off the renewable energy systems, various circuit breakers located inthe distribution panel, etc.

The renewable energy agent 74 is responsible for the following:

-   -   Prediction of the energy generation from the renewable systems.    -   Communication with the power and lighting system coordinator 66.        it sends energy generation predictions and receives commands        from the power and lighting system coordinator 66.

b. Internal Structure

Referring to FIG. 7, the internal structure of the renewable energyagent 74 and its related environment is shown. The environment for therenewable energy agent 74 is comprised of all the sensors 116, 118, 120,122, actuators 124, 126, 128, 130, and renewable energy systems. Theagent 74 is comprised of five modules, each with its own dedicatedalgorithms and control logic.

The data filtering module 132 is responsible for separating the datareceived from sensors 116, 118, 120, 122 and actuators 124, 126, 128,130. For example, the amount of stored or generated data will be sent tothe system feedback module 134, while data from other various sensors(e.g. alarms, battery levels etc.) will be sent to the system analysisand control module 136. The data filtering module 132 may also send tothe system analysis and control module 136 the same data that was sentto the system feedback module 134.

The system feedback module 134 is responsible for the following:

-   -   Collection of information from the various sensors 116, 118,        120, 122 and actuators 124, 126, 128, 130. The type of        information that the system feedback module 134 may collect is        described in the data filtering module 132.    -   Analysis of predictions related to the energy generated or        stored. The data received from the various sensors 116, 118,        120, 122 and actuators 124, 126, 128, 130 will be compared        against the predictions made by the machine learning module 138.        Any large discrepancies between the predicted and measured        values could mean that the previous commands could indicate that        the difference in the error or accuracy of the predictions made        by the machine learning module 138 and the actual outcome might        have exceeded critical limits, which in turn has affected the        overall energy and energy performance of the renewable energy        system. The system feedback module 134 will then send the        measured values to the machine learning module 138 such that the        machine learning module 138 can update its internal learning        algorithms.    -   Collection of information from the power and lighting system        coordinator 66. The information may represent feedback regarding        the predictions that the renewable energy agent 74 has made or        regarding the commands that the power and lighting system        coordinator 66 might have previously sent. For example, the        machine learning module 138 might have predicted that a certain        amount of renewable energy will be available for use by the        power systems. However, the actual amount of generated energy is        greater than what was predicted. Once it detects this        difference, the power and lighting system coordinator 66 may        command the renewable energy agent 74 to improve its internal        learning algorithms such that the error of its predictions is        minimized to the greatest extent possible.

The machine learning module 138 is responsible for the following:

-   -   Collection of information from the system feedback module 134.        The type of information that this module will collect is        described under the system feedback module 134.    -   Collection of information from the system analysis and control        module 136. This information may represent data from the various        sensors 116, 118, 120, 122 and actuators 124, 126, 128, 130.    -   Making predictions, based on the data received from the system        analysis and control module 136 or from the system feedback        module 134. For example, the machine learning module 138, using        weather data and data from the renewable energy systems (e.g.        generation capacity, storage capacity, etc.) may be able to        predict the rate at which energy will be generated or stored by        the renewable energy systems. Further, the machine learning        module 138 may predict how much energy will be generated in the        next hour, next day, week or month.    -   Sending the predictions to the system analysis and control        module 136 and to the scenario generator module 140.

The system analysis and control module 136 is responsible for thefollowing:

-   -   Monitoring and control of all sensors 116, 118, 120, 122 and        actuators 124, 126, 128, 130 related to the renewable energy        systems.    -   Collection of information from the data filtering module 132.        The type of information that this module will collect is        described under the data filtering module 132.    -   Analyzing the data from each sensor and actuator. For example,        the system analysis and control module 136 may determine that a        solar panel or an array of solar panels is no longer capable of        functioning as required. As such, the system analysis and        control module 136 may need to send this feedback to the machine        learning module 138 for predictions.    -   Analyzing the scenarios proposed by the scenario generator        module 140. For each scenario received, the system analysis and        control module 136 will analyze the status of the sensors and        actuators and will determine which scenario is valid. For        example, one of the scenarios that the scenario generator module        140 may generate is to predict how much energy the renewable        systems can generate if there are clear sky conditions and if        90% of the solar panels are not available for use. The system        analysis and control module 136 may determine that, due to        additional solar panel failures, such scenario is no longer        valid and that the machine learning module 138 will not make a        prediction on the referenced scenario.

The scenario generator module 140 is responsible for continuouslylooking for ways/scenarios to improve the overall energy performancerenewable energy systems. For example, the scenario generator module 140may create a series of scenarios that will then be sent to the systemanalysis and control module 136 to analyze and validate. Once thescenarios are validated, they may be sent to the machine learning module138 to make predictions on. The predictions made by the machine learningmodule 138 will then be sent back to the scenario generator module 140for analysis. After analyzing the predictions, the scenario generatormodule 140 may decide to send such predictions to the power and lightingsystem coordinator 66, which may send them to SMITHGROUP-AI 10, which inturn may direct the power and lighting system coordinator 66 toimplement one of the scenarios created by the scenario generator module140.

The scenario generator module 140 may create scenarios by simulating theamount of energy generated or stored by the renewable energy system, bysimulating the demand that the power system is exercising on therenewable energy system, by simulating various outdoor conditions (e.g.cloudy sky, wind speeds, etc.), by simulating various rates of energygeneration (e.g. how much kWh are being generated in the next 3 hours),or by simulating various system settings (e.g. angle and direction ofthe solar panels, direction of the windmill.)

5. Utility Agent

The utility agent's sole responsibility is to monitor the status of theutility power (e.g. loss of power) or receive information from theutility company to enter into demand response mode and notify the powerand lighting system coordinator 66 of such events.

D. AHU System

1. General Description

Referring to FIG. 8, an airside system is shown as having one airhandling unit 142 delivering a mixture of outside air and return air tofive zones 144, 146, 148, 150, 152. The control of the entire airsidesystem is performed through a series of independent software agents suchas the AHU system coordinator 20, AHU agent 154, and zone agents 156,158, 160, 162, 164.

FIG. 9 shows the communication architecture between the various airsidesystem agents. Considering the network architecture of the sensors andactuators associated with an AHU, all sensors and actuators areconnected directly to the network, without the use of proprietarycontrollers that operate with programmed sequences of operation. In someinstances, an open source non-proprietary input/output module or agateway may be required to convert the signal from a sensor or anactuator such that it can be communicated via open source networks suchas BACnet, LONworks, Modbus etc.

2. AHU System Coordinator

a. Purpose

The AHU system coordinator 20 is an independent software agent thatmonitors and controls all agents 154, 156, 158, 160, 162, 164 associatedwith its respective airside system. Further, the AHU system coordinator20 is responsible for the following:

-   -   Prediction of the energy use of the entire airside system.    -   Communication with SMITHGROUP-AI 10. It sends overall airside        system energy use predictions and receives commands from        SMITHGROUP-AI 10.    -   Communication with the AHU agent 154. it requests energy        consumption predictions and status from the ahu agent 154 and        sends predictions (e.g. total system supply airflow, total        system return air flow, total system outside airflow) such that        the AHU agent 154 can make associated predictions related to the        energy performance of the AHU 142.    -   Communication with the hot water system coordinator 18 and the        chilled water system coordinator 16. The type of information        that the AHU system coordinator 20 communicates to the hot water        system coordinator 18 and the chilled water system coordinator        16 may represent cooling load and heating load predictions from        each zone agent 156, 158, 160, 162, 164 and AHU agent 154.

b. Internal Structure

Referring to FIG. 10, the internal structure of the AHU systemcoordinator 20 and its related environment is shown. The environment forthe AHU system coordinator 20 is comprised of all the agents that itmonitors and controls. The agent is comprised of five modules, each withits own dedicated algorithms and control logic.

The data filtering module 166 is responsible for separating the datareceived from the various agents 154, 156, 158, 160, 162, 164. Forexample, the actual agent energy consumption levels or actual agentairflows will be sent to the system feedback module 168, whilepredictions from the agents 154, 156, 158, 160, 162, 164 will be sent tothe system analysis and control module 170.

The system feedback module 168 is responsible for the following:

-   -   Collection of information from the zone agents 156, 158, 160,        162, 164 and ahu agent 154. The information may represent status        (e.g. failed, maintenance), or actual agent energy consumptions        levels from the various agents. A failed status may indicate        that a zone agent 156, 158, 160, 162, 164 or an AHU agent 154 no        longer controls its environment and should be excluded from the        overall airside system predictions that the machine learning        module 172 is making. A maintenance status may indicate that a        zone agent 156, 158, 160, 162, 164 or an AHU agent 154 has        entered or may enter into a maintenance mode and that the        machine learning module 172 needs to update its overall airside        system predictions accordingly. The information may also        represent predictions related to the energy consumption levels        of each agent.    -   Analysis of predictions related to the total airside system        airflow. The actual airflows from the various agents 154, 156,        158, 160, 162, 164 will be compared against their own        predictions but also against the predictions made by the machine        learning module 172. Any large discrepancies between the        predicted and actual airflows could mean that the previous        command that a zone agent 156, 158, 160, 162, 164 or AHU agent        154 received from the AHU system coordinator 20 has placed a        zone agent 156, 158, 160, 162, 164 or AHU agent 154 in conflict        with its own internal laws (e.g. not able to meet the various        zone thermal requirements). This could indicate that the        difference in the error or accuracy of the predictions made by        the machine learning module 172 and the actual outcome might        have exceeded critical limits, which in turn has affected the        overall energy and thermal performance of the airside system.        The system feedback module 168 will then send the actual        airflows to the machine learning module 172 such that the        machine learning module 172 can update its internal learning        algorithms.    -   Collection of information from SMITHGROUP-AI 10: the information        may represent commands that need to be distributed to the        various associated zone agents 154, 156, 158, 160, 162, 164 and        AHU agents 154. The information may also represent feedback        regarding the predictions that the AHU system coordinator 20 has        made; for example, the predicted energy performance of the        scenario that SMITHGROUP-AI 10 has directed the AHU system        coordinator 20 to implement is significantly different that the        real outcome. As such the system feedback module 168 will pass        this feedback to the machine learning module 172 to update its        machine learning algorithms accordingly such that the accuracy        or error of its predictions self-improves.

The machine learning module 172 is responsible for the following:

-   -   Collection of information from the system feedback module 168.        The type of information that this module will collect is        described under the system feedback module 168.    -   Collection of information from the system analysis and control        module 170. This information may represent predictions from zone        agents 156, 158, 160, 162, 164 and AHU agent 154 regarding        airflows, temperature, cooling loads, heating loads, etc.    -   Making predictions, using machine learning algorithms, based on        the data received from the system analysis and control module        170, for the overall energy consumption of the entire airside        system or for the total airflow that the AHU agent 154 will need        to deliver.    -   Sending the predictions to the system analysis and control        module 170 and to the scenario generator module 174.

The system analysis and control module 170 is responsible for thefollowing:

-   -   Collection of information from the data filtering module 166.        The type of information that this module will collect is        described under the data filtering module 166.    -   Analyzing the data from each agent, and filtering and compiling        the data into valid system scenarios. A more detailed        description of this process is presented in the sample process        paragraph.    -   Sending the valid system scenarios to the machine learning        module 172 for predictions.    -   Receiving the predictions from the machine learning module 172        and sending commands to the various agents 154, 156, 158, 160,        162, 164. The commands that the system analysis and control        module 170 may send to the agents could be airflow setpoints,        temperature setpoints or requests for predictions.    -   Analyzing the scenarios proposed by the scenario generator        module 174. For each scenario received, the system analysis and        control module 170 may decide to direct the various agents to        make predictions and then compile these predictions into valid        system scenarios for the machine learning module 172 to make        predictions on.

The scenario generator module 174 is responsible for continuouslylooking for ways to improve the overall energy performance of the entireairside system. For example, the scenario generator module 174 maycreate a series of scenarios that will then be sent to the systemanalysis and control module 170 to analyze and validate. The systemanalysis and control module 170 may ask the agents to make predictionson the scenarios. Once the scenarios are validated, they may be sent tothe machine learning module 172 to make predictions on. the predictionsmade by the machine learning module 172 will then be sent back to thescenario generator module 174 for analysis. After analyzing thepredictions, the scenario generator module 174 may decide to send suchpredictions to SMITHGROUP-AI 10, which in turn may direct the AHU systemcoordinator 20 to implement one of the scenarios created by the scenariogenerator module 174.

The scenario generator module 174 may create scenarios by modelling theAHU agent 154 as delivering various airflows and various temperaturesand by modelling the zone agents 156, 158, 160, 162, 164 as satisfyingtheir zone thermal load conditions under various conditions.

c. Sample Process

Each zone agent 156, 158, 160, 162, 164 will send to the AHU systemcoordinator 20 a series of predictions and status information. Thepredictions that each zone agent 156, 158, 160, 162, 164 will send tothe AHU system coordinator 20 are related to the airflow requirementsand various temperatures that the zone agent could use to satisfy itszone requirements. Further, each zone agent 156, 158, 160, 162, 164 willsend to the AHU system coordinator 20 the associated zone predictionsfor the hot water system coordinator 18 and for the chilled water systemcoordinator 16.

The system analysis and control module 170 will first compile allairside system related predictions from the zone agents 156, 158, 160,162, 164. The system analysis and control module 170, based on the lawsof each zone agent 156, 158, 160, 162, 164, will then eliminate any zoneagent predictions that will make other zone agents incapable of meetingtheir internal laws. The system analysis and control module 170 willthen compile and sort all valid predictions made by the zone agents 156,158, 160, 162, 164. For example, as a first step, the system analysisand control module 170 may sort the predictions based on a common AHUdischarge air temperature. In a second step, the system analysis andcontrol module may then choose the lowest dew point temperature requiredby a zone agent 156, 158, 160, 162, 164. The system analysis and controlmodule 170 will then establish a series of valid airside systemscenarios. These scenarios will then be sent to the machine learningmodule 172 to make predictions regarding the total system supplyairflow, return airflow return air temperature, and outside airflow. Forexample, by analyzing the various zone ventilation efficiencies and theoutside air-dry bulb temperature and relative humidity, the machinelearning module 172 may predict that an air side economizer (e.g. anartificial increase in outside air flow) is better suited thandelivering the lowest required amount of outside air flow. The machinelearning module 172 will send these predictions back to the systemanalysis and control module 170, which in turn will send them to the AHUagent 154 to make its own predictions regarding AHU energy consumption.Once it receives the predictions from the AHU agent 154, it will thensend the overall airside system predictions to SMITHGROUP-AI 10 and theassociated zone agent and AHU agent load predictions to the hot watersystem coordinator 18 and the chilled water system coordinator 16. TheAHU system coordinator 20 will send to the hot water system coordinator18 only the zone agent predicted heating loads that correspond to validscenarios analyzed by the system analysis and control module 170. TheAHU system coordinator 20 will send to the chilled water systemcoordinator 16 only the AHU agent predicted cooling loads thatcorrespond to total system airflow predictions performed by the machinelearning module 172.

In further detail, each zone agent creates a series of scenariosassociated with the zone that it is serving and sends these scenarios toa system coordinator (e.g. AHU system coordinator 20). These scenariosare given a unique ID number (Table 1 below) based on a specific airflowsupply air temperature (SAT) and a specific entering water temperature(EWT).

TABLE 1 Zone Agent 1 Predictions Zone Data Primary Temperature Coil DewPoint Zone Outdoor Energy Zone Zone Load Setpoint EAT DP Airflow AirFraction Predictions Prediction ID Btu/Hr 0F 0F 0F CFM Zp kWh Agent_1-120000 72 50 48 1000 0.76 0.10 Agent_1-2 20000 72 51 49 1200 0.92 0.36Agent_1-3 20000 72 52 50 1400 0.95 0.37 Agent_1-4 20000 72 53 51 16000.67 0.43 Agent_1-5 20000 72 54 52 1800 0.81 0.18 Agent_1-6 20000 72 5553 2000 0.91 0.40 Agent_1-7 20000 72 56 54 2200 0.78 0.39 Agent_1-820000 72 57 55 2400 0.70 0.24 Agent_1-9 20000 72 58 56 2600 0.68 0.29Agent_1-10 20000 72 59 57 2800 0.81 0.12

The AHU system coordinator 20 then filters/validates the scenariosreceived from the zone agents (Table 2 below). The validation processcould be based on the physical limitation of the air handling unit. Insome instances, even though a zone may be able to be cooled with a verycold air temperature (e.g. 50° F.), due the operating conditions at thattime or physical limitations of the internal components of the airhandling unit, the air handling unit may not be able to cool the air to50° F. As such the AHU system coordinator 20 may label the associatedscenarios as invalid (in bold below).

TABLE 2 Zone Agent 1 Predictions Primary Zone Data Outdoor Zone ZoneCoil Dew Point Zone Air Energy Prediction Load Temperature EAT DPAirflow Fraction Prediction ID BTU/Hr Setpoint OF OF OF CFM Zp kWhAgent_1-1 2000 72 50 48 1000 .76 .10 Agent_1-2 2000 72 51 49 1200 .92.36 Agent_1-3 2000 72 52 50 1400 .95 .37 Agent_1-4 2000 72 53 51 1600.67 .43 Agent_1-5 2000 72 54 52 1800 .81 .18 Agent_1-6 2000 72 55 532000 .91 .40 Agent_1-7 2000 72 56 54 2200 .78 .39 Agent_1-8 2000 72 5755 2400 .70 .24 Agent_1-9 2000 72 58 56 2600 .68 .29 Agent_1-10 2000 7259 57 2800 .81 .12

The AHU system coordinator 20 will then start creating combinations ofagent scenarios (Table 3 below). Each combination of scenarios is thengiven a unique ID number (e.g. AHU_1-1). For each combination of agentscenarios there is a corresponding set of air handling unit conditionsthat need to be achieved (e.g. total airflow, coil dew point, etc.).

TABLE 3 AHU-1 System Predictions Zone Predictions System IdentificationData Primary Temperature Coil Dew Point Zone Outdoor Energy AHU SystemZone Setpoint EAT DP Airflow Air Fraction Predictions Scenario IDPrediction ID 0F 0F 0F CFM Zp kWh AHU_1-1 Agent_1-3 72 52 50 1400 0.950.37 Agent_2-3 70 52 50 700 0.95 0.37 Agent_3-1 73 52 48 300 0.76 0.10Agent_4-3 71 52 50 1200 0.95 0.37 Agent_5-3 75 52 50 1400 0.95 0.37Totals 5000

The AHU system coordinator 20 will then send these combinations of agentscenarios (Table 4 below) to the AHU agent 154 to make predictions onthe total system supply airflow, return airflow, and outside air flow.

TABLE 4 AHU-1 SYSTEM PREDICTIONS AHU SYSTEM SCENARIOS PREDICTIONS TotalTotal Total AHU Supply Air Dew Zone System System System SystemTemperature Point Supply Supply Return Return Air Outside Prediction SATDP Airflow Airflow Airflow Temperature Airflow ID OF OF CFM CFM CFM OFCFM AHU_1-1 52 48 5000 5300 4900 71 1000 AHU_1-2 53 49 5700 5700 5000 732000 AHU_1-3 54 50 6400 6900 6000 75 3000 AHU_1-4 55 51 7100 7800 700074 4000

After receiving the predictions from the AHU agent 154, the AHU systemcoordinator 20 will then make predictions on the overall energyconsumption levels for each combination of agent scenarios (Table 5below) and will send these predictions to SMITHGROUP-AI 10.

TABLE 5 AHU-1 AGENT PREDICTIONS RECEIVED DATA AHU-1 SYSTEM SCENARIOSTotal Total PREDICTION AHU Supply Air Dew System System System AHU-1System Temperature Point Supply Return Return Air Outside EnergyPrediction SAT DP Airflow Airflow Temperature Airflow Consumption ID OFOF CFM CFM OF CFM kWh AHU_1-1 52 48 5300 4900 71 1000 10 AHU_1-2 53 495700 5000 73 2000 15 AHU_1-3 54 50 6900 6000 75 3000 20 AHU_1-4 55 517800 7000 74 4000 25

3. AHU Agent

a. Purpose

The AHU agent 154 is an independent software agent that monitors andcontrols all sensors and actuators associated with an AHU. Each AHU iscontrolled and monitored by a dedicated AHU agent. Further, the AHUagent 154 is responsible for the following:

-   -   Prediction of the energy use of the entire airside system.    -   Prediction of the required heating coil loads and cooling coil        loads.    -   Prediction of the air pressure drop through each of the AHU        components.    -   Prediction of the supply fan array energy consumption.    -   Prediction of the return fan array energy consumption.    -   Communication with the AHU system coordinator 20. It sends        overall AHU energy use predictions and receives commands from        the AHU system coordinator 20.

b. Internal Structure

Referring to FIG. 11, the internal structure of the AHU agent 154 andits related environment is shown. The environment for the AHU agent 154is comprised of the sensors 176, 178, 180, 182 and actuators 184, 186,188, 190 that are used to monitor and control the AHU 142. The agent 154is comprised of five modules, each with its own dedicated algorithms andcontrol logic.

The data filtering 192 module is responsible for separating the datareceived from sensors 176, 178, 180, 182 and actuators 184, 186, 188,190. For example, the energy consumption levels of fans (measured bytheir related variable frequency drives (VFD)), actual fan airflows(measured by the associated airflow stations), or various temperatures(e.g. cooling coil leaving air temperature, unit leaving airtemperature, etc.) will be sent to the system feedback module 194, whiledata from other various sensors (e.g. alarms, temperatures, etc.) willbe sent to the system analysis and control module 196. The datafiltering module 192 may also send to the system analysis and controlmodule 196 the same data that was sent to the system feedback module194.

The system feedback 194 module is responsible for the following:

-   -   Collection of information from the various sensors and        actuators. The type of information that the system feedback        module 194 may collect is described in the data filtering module        192.    -   Analysis of predictions related to various temperatures (e.g.        mixed air temperatures, coil leaving air temperature, unit        leaving air temperature, etc.). The data received from the        various sensors 176, 178, 180, 182 and actuators 184, 186, 188,        190 will be compared against the predictions made by the machine        learning module 198. Any large discrepancies between the        predicted and measured values could mean that the previous        commands could indicate that the difference in the error or        accuracy of the predictions made by the machine learning module        198 and the actual outcome might have exceeded critical limits,        which in turn has affected the overall energy and energy        performance of the air handling unit 142. The system feedback        module 194 will then send the measured values to the machine        learning module 198 such that the machine learning module 198        can update its internal learning algorithms.    -   Collection of information from the AHU system coordinator 20.        The information may represent feedback regarding the predictions        that the AHU agent 154 has made. For example, the AHU 142 might        deliver a certain amount of supply air to the zones 144, 146,        148, 150, 152 and at the same time its internal airflow sensors        are confirming that the measured supply airflow is equal to or        close to what was predicted. However, some zones, as measured by        the sensors of the zone agents 156, 158, 160, 162, 164 might not        receive sufficient airflow. This could be indication that the        AHU airflow sensors are defective or that they might need        recalibration.

The machine learning module 198 is responsible for the following:

-   -   Collection of information from the system feedback module 194.        The type of information that this module will collect is        described under the system feedback module 194.    -   Collection of information from the system analysis and control        module 196. This information may represent data from the various        sensors 176, 178, 180, 182 and actuators 184, 186, 188, 190,        such as airflows and associated temperatures.    -   Making predictions, using machine learning algorithms, based on        the data received from the system analysis and control module        196 or from the system feedback module 194.    -   Sending the predictions to the system analysis and control        module 196 and to the scenario generator module 200.

For each component (e.g. coil, filter bank, fans, etc.) of the AHU 142,the machine learning module 198 will use various machine learningalgorithms to predict its performance (e.g. Btu/hr, leaving watertemperature, water flow, etc.), air pressure drop, energy consumption(e.g. kWh). For example, a coil has fixed properties (e.g. width,height, number of fins, tube diameter, etc.). As such, a machinelearning algorithm may be trained, via the use of manufacturer's ratedcoil performance data at various conditions (e.g. various entering airtemperatures, entering water temperatures and flow), to predict what thecoil leaving air temperature and water temperature may be at otherconditions not included in the training data set.

Similarly, using fan laws and manufacturer's rated fan performance dataas a training set, a machine learning algorithm may be trained topredict what the fan energy consumption will be at various systemconditions (e.g. various airflows and various associated fan staticpressures). Once released into the real environment, the machinelearning module 198, via the feedback received from the system feedbackmodule 194, may update its internal machine learning algorithms andrelated predictions (e.g. fan motor energy consumption, static pressuresetpoints, etc.) to account for system effects and for the measurementerrors of the various sensors.

Using an approach similar to the above, the machine learning module 198may also use each individual component prediction as a data set and/ortraining set to make predictions for the energy consumption of theentire AHU 142. For example, the machine learning module 194 may build atraining set comprised of the supply fan airflow, return fan airflow,return static pressure, supply static pressure, and associated fan motorenergy consumption. Once the training set is of considerable size, themachine learning module 198 may use it to predict fan motor energyconsumption on new data that is not a part of the training set.

The machine learning module 198 may also inform the system analysis andcontrol module 196 on what coil (e.g. preheat coil or cooling coil) canbe used for preheating. For example, the machine learning module 198,using various sensors (e.g. return air and outside temperature sensors),may predict that the mixed air temperature resulted from the mixing ofthe return air flow and outside airflow is 37° F., while the requiredAHU supply air temperature is 52° F. A typical approach would be toenable the preheat coil to warm up the mixed air around 50° F., andthen, by picking up the heat from the fan motors, the AHU supply airtemperature will be 52° F. However, the machine learning module 198, byanalyzing the properties of the cooling coil and the related chilledwater temperatures and flow, may predict that the preheat coil isrequired to only preheat the mixed air to 47° F. and then the chilledwater coil can be used to preheat the air to 50° F. In the case whenpreheating is not required, the machine learning module 198 may predictthat an artificial increase in the amount of outside air that the AHU142 is delivering may lower the mixed air return air temperature enoughto require preheating using the cooling coil. This approach may resultin significant energy savings for both the AHU 142 and the chilled waterplant. The predictions described above will then be sent to the AHUsystem coordinator 20, which in turn will send the predictions to thechilled water system coordinator 16. See chilled water systemcoordinator description for additional information.

The system analysis and control module 196 is responsible for thefollowing:

-   -   Monitoring and control of all sensors and actuators related to        the AHU 142.    -   Collection of information from the data filtering module 192.        The type of information that this module will collect is        described under the data filtering module 192.    -   Analyzing the data from each sensor and actuator. For example,        the system analysis and control module 196 may determine that a        fan is no longer capable of running (e.g. the motor has burned        out). As such, the system analysis and control module 196 may        need to update its valid scenario (e.g. how many fans need to        operate and at what speed) to deliver the required amount of        airflow to the zones 144, 146, 148, 150, 152.    -   Sending the valid system scenarios to the machine learning        module 198 for predictions.    -   Receiving the predictions from the machine learning module 198        and sending commands to the various actuators 184, 186, 188,        190. The commands that the system analysis and control module        196 may send to the actuators 184, 186, 188, 190 could be valve        position, damper position or fan speed.    -   Analyzing the scenarios proposed by the scenario generator        module 200. For each scenario received, the system analysis and        control module 196 will analyze the status of the current        sensors 176, 178, 180, 182 and actuators 184, 186, 188, 190 and        will determine which scenario is valid. For example, one of the        scenarios that the scenario generator module 200 may generate,        will require the AHU 142 to deliver 10,000 cfm to the zones 144,        146, 148, 150, 152. The system analysis and control module 196        may determine that, due to a fan failure, such scenario is no        longer valid and that the machine learning module 198 will not        make a prediction on the referenced scenario.

The scenario generator module 200 is responsible for continuouslylooking for ways/scenarios to improve the overall energy performance ofthe AHU 142. For example, the scenario generator module 200 may create aseries of scenarios which will then be sent to the system analysis andcontrol module 196 to analyze and validate. Once the scenarios arevalidated, they may be sent to the machine learning module 198 to makepredictions on. The predictions made by the machine learning module 198will then be sent back to the scenario generator module 200 foranalysis. After analyzing the predictions, the scenario generator module200 may decide to send such predictions to the AHU system coordinator20, which may send them to SMITHGROUP-AI 10, which in turn may directthe AHU system coordinator 20 to implement one of the scenarios createdby the scenario generator module 200.

The scenario generator module 200 may create scenarios by varying theAHU air flows (e.g. outside air flow, return airflow, etc.) and relatedtemperatures Each such scenario will have an impact on the energyperformance of fan motors and on the AHU coil heating and cooling loads.

E. Heating Hot Water System

1. General Description

FIG. 12 represents the heating plant 202 providing heating hot water toone air handling unit 204 and seven thermal zones 206, 208, 210, 212,214, 216, 218. The control of the entire hot water system is performedthrough a series of independent software agents such as the hot watersystem coordinator 18, heating plant agent 220, and zone agents 222,224, 226. The communication architecture between the various agents andcoordinators is shown in FIG. 13.

2. Hot Water System Coordinator

a. Purpose

The hot water system coordinator 18 is an independent software agentthat monitors and controls all agents associated with its respectiveheating system. By coordinating and predicting the energy usage of theentire heating system, the hot water system coordinator 18 will informSMITHGROUP-AI 10 with the necessary information for it to optimize theoverall building energy use.

The hot water system coordinator 18 is responsible, but not limited tothe following:

-   -   Satisfy and maintain all building heating loads requirements.    -   Optimize the energy use of the heating system.    -   Predict the energy use of the heating system.    -   Communication with SMITHGROUP-AI 10. It sends overall heating        system energy use predictions and receives commands from        SMITHGROUP-AI 10.    -   Communication with the hot water plant agent 220. It requests        energy consumption predictions and status from the hot water        plant agent 220 and sends predictions (e.g. total heating hot        water system water flow, heating hot water supply temperature,        heating hot water return temperature) such that the heating        plant agent 220 can make associated predictions related to the        energy performance of the heating water plant 220.

b. Internal Structure

FIG. 14 represents the internal structure of the hot water system (HWS)coordinator 18 and its environment. The environment of the HWScoordinator 18 is comprised of all agents 222, 224, 226, 228, 230, 232,234, 236 that it monitors and controls. The agents that the HWScoordinator 18 controls are all zones and AHUs that contain a hot watercoil and require hot water from the heating plant. The agent iscomprised of five unique modules that have its own dedicated algorithm.

The data filtering module 238 is responsible for separating the datareceived from the various associated agents 222, 224, 226, 228, 230,232, 234, 236. For example, the actual agent energy consumption levelsor actual agent hot water flow may be sent to the system feedback module240, while predictions from the agents will be sent to the systemanalysis and control module 242.

The system feedback module 240 is responsible for the following:

-   -   Collection of information from the zone agents 222, 224, 226,        228, 230, 232, 234 and the heating plant agent 220; the        information may represent status (e.g. failed, maintenance), or        actual agent energy consumptions levels from the various agents.        A failed status may indicate that a zone agent 222, 224, 226,        228, 230, 232, 234 may no longer control its environment and        should be excluded from the overall condenser water system        predictions that the machine learning module 244 is making. A        maintenance status may indicate that a zone agent 222, 224, 226,        228, 230, 232, 234 has entered or may enter into a maintenance        mode and that the machine learning module 244 needs to update        its overall chilled water system predictions accordingly. The        information may also represent predictions related to the energy        consumption levels of each agent.    -   Analysis of predictions related to the total heating hot water        flow. The actual heating hot water flow from the various agents        will be compared against their own predictions but also against        the predictions made by the machine learning module 244. Any        large discrepancies between the predicted and actual heating hot        water flows could mean that the previous command that a zone        agent 222, 224, 226, 228, 230, 232, 234 or the heating plant        agent 220 received from the hot water system coordinator 18 has        placed an agent 222, 224, 226, 228, 230, 232, 234, 236 in        conflict with its own internal laws (e.g. not able to meet the        various zone thermal requirements or expected chilled water        system flow). This could indicate that the difference in the        error or accuracy of the predictions made by the machine        learning module 244 and the actual outcome might have exceeded        critical limits, which in turn has affected the overall energy        and thermal performance of the heating system. The system        feedback module 240 will then send the actual chilled water        flows to the machine learning module 244 such that the machine        learning module 244 can update its internal learning algorithms.    -   Collection of information from SMITHGROUP-AI 10. The information        may represent commands that need to be distributed to the        various associated agents. The information may also represent        feedback regarding the predictions that the hot water system        coordinator 18 has made; for example, the predicted energy        performance of the scenario that SMITHGROUP-AI 10 has directed        the hot water system coordinator 10 to implement is        significantly different than the real outcome. As such the        system feedback module 240 will pass this feedback to the        machine learning module 244 to update its machine learning        algorithms accordingly such that the accuracy or error of its        predictions self-improves.

The machine learning module 244 will be responsible for the following:

-   -   Collect information from the data filtering 238 and system        analysis and control module 242.    -   Predict the overall energy consumption of the entire hot water        system or for the total water flow that the heating plant agent        220 must deliver based on the data received from the system        analysis and control module 242.    -   Send predictions back to the system analysis and control module        242 and to the scenario generator module 246.

The system analysis and control module 242 is responsible for thefollowing:

-   -   Collect information from the data filtering module 238. The type        of information that this module may collect is described under        the data filter module 238.    -   Analyze data received from each agent, and filtering and        compiling the data into valid system scenarios. A more detailed        description of this process is presented in the sample process.    -   Send valid system scenarios to the machine learning module 244        for predictions.    -   Receiving the predictions from the machine learning module 244        and send commands to the various agents. The commands that the        system analysis and control module 242 may send to the agents        222, 224, 226, 228, 230, 232, 234, 236 could be chilled water        entering and leaving setpoints, heating hot water flows, or        requests for predictions.    -   Analyzing the scenarios proposed by the scenario generator        module 246. For each scenario received, the system analysis and        control module 242 may decide to direct the various agents to        make predictions and then compile these predictions into valid        system scenarios for the machine learning module 244 to make        predictions on.

The scenario generator module 246 is responsible for continuouslylooking for ways to improve the overall energy performance of the entireheating hot water system. For example, the scenario generator module 246may create a series of scenarios which will then be sent to the systemanalysis and control module 242 to analyze and validate. The systemanalysis and control module 242 may ask the associated agents to makepredictions on the scenarios. Once the scenarios are validated, they maybe sent to the machine learning module 244 to make predictions on. Thepredictions made by the machine learning module 244 will then be sentback to the scenario generator module 246 for analysis. After analyzingthe predictions, the scenario generator module 246 may decide to sendsuch predictions to SMITHGROUP-AI 10, which in turn may direct the hotwater system coordinator 18 to implement one of the scenarios created bythe scenario generator module 246.

The scenario generator module 246 may create scenarios by modelling theheating plant agent 220 as delivering various heating hot water flowsand associated temperatures and by modelling the zone agents 222, 224,226, 228, 230, 232, 234 or AHU agents 236 as satisfying their loadconditions under various conditions.

3. Heating Plant Agent

a. Purpose

The heating plant agent 220 is an independent agent that monitors andcontrols all sensors 248, 250, 252, 254 and actuators 256, 258, 260,262, associated with the heating plant 202. Further, the heating plantagent 220 is responsible for the following:

-   -   Prediction of the energy use of the entire heating plant.    -   Prediction of the boiler's energy consumption.    -   Prediction of firing rates of each boiler.    -   Prediction of the hot water pumps energy consumption.    -   Prediction of the water pressure drop through each of the hot        water system piping components (e.g. control valve, heat        exchanger, coil).    -   Communication with the hot water system coordinator 18. It        receives valid scenario predictions (e.g. total hot water coil        loads, hot water supply temperature, hot water return        temperature, total hot water flow) such that the hot water agent        can make associated energy predictions to send back to the hot        water system coordinator 18.

b. Internal Structure

As shown in FIG. 15 the internal structure of the heating plant agent220 includes five modules: data filtering 264, system feedback 266,machine learning 268, system analysis and controls 270, and scenariogenerators 272. The environment for the heating plant agent 220 iscomprised of all the sensors 248, 250, 252, 254 and actuators 256, 258,260, 262 of the equipment that it monitors and controls. The sensors248, 250, 252, 254 and actuators 256, 258, 260, 262 that are part of theheating plant agent's environment are connected directly to the network,without the use of proprietary controllers that operate with programmedsequences of operation. In some instances, an open sourcenon-proprietary input/output module or a gateway may be required toconvert the signal from a sensor or an actuator such that it can becommunicated via open source networks such as BACnet, LONworks, Modbusetc.

The data filtering module 264 is responsible for separating the datareceived from the sensors 248, 250, 252, 254 and actuators 256, 258,260, 262. For instance, the energy consumption levels of the hot waterpumps (measured by the variable frequency drives (VFD)), actual heatinghot water flows (measured by the associated flow meters), actual naturalgas consumption (measured by the associated boiler natural gas flowmeters), or the actual plant hot water return temperature may be sent tothe system feedback module 266. Other data such as status and alarms maybe sent to the system analysis and control module 270. The datafiltering module 264 may also send to the system analysis and controlmodule 270 the same data that was sent to the system feedback module266.

The system feedback module 266 is tasked with the following:

-   -   Collection of information from the various sensors 248, 250,        252, 254 and actuators 256, 258, 260, 262. The type of        information that the system feedback module 266 may collect is        described in the data filtering module 264.    -   Analysis of predictions related to various temperatures (e.g.        plant heating hot water leaving temperature, plant heating hot        water return temperature, etc.). The data received from the        various sensors 248, 250, 252, 254 and actuators 256, 258, 260,        262 will be compared against the predictions made by the machine        learning module 268. Any large discrepancies between the        predicted and measured values could mean that the previous        commands could indicate that the difference in the error or        accuracy of the predictions made by the machine learning module        268 and the actual outcome might have exceeded critical limits,        which in turn has affected the overall energy and energy        performance of the heating plant. The system feedback module 266        will then send the measured values to the machine learning        module 268 such that the machine learning module 268 can update        its internal learning algorithms.    -   Collection of information from the hot water system coordinator        18. The information may represent feedback regarding the        predictions that the heating plant agent 220 has made. For        example, the heating plant 202 might deliver a certain heating        hot water flow to the zones 206, 208, 210, 212, 214, 216, 218 or        to the AHUs 204 in the same time its internal flow meters are        confirming that the measured heating hot water flow is equal to        or close to what was predicted. However, some zones 206, 208,        210, 212, 214, 216, 218 or AHUs 204, as measured by their        associated flow meters, might not receive sufficient heating hot        water; this could be indication that the plant heating hot water        flow meters are defective or that they may need recalibration.

The machine learning module 268 is tasked with the following:

-   -   Collect information from the data filtering 264 and system        analysis and control module 270.    -   Predict the overall energy consumption of the entire heating        plant or the total heating hot water flow that the heating plant        agent 220 will need to deliver based on the data received from        the system analysis and control module 270.    -   Send the predictions back to the system analysis and control        module 270 and to the scenario generator module 272.    -   Predict heating energy consumption, leaving water temperature,        water flows for each major component (e.g. boiler, pumps etc).

Using manufacturers rated boiler performance and boiler curves data as atraining set, a machine learning algorithm may be trained to predict theboiler energy consumption at various system conditions (e.g. enteringand leaving hot water temperatures, hot water flows etc.). Once releasedinto the real environment, the machine learning module 268, via thefeedback received from the system feedback module 266, may update itsinternal machine learning algorithms and related predictions to accountfor system effects and the measurement errors of the various sensors.

Pump data will also be updated similarly. Using pump laws andmanufacturer's rated pump performance data as a training set, a machinelearning algorithm may be trained to predict what the pump energyconsumption will be at various system conditions, water flow, and pumphead conditions. Once released into the environment, the machinelearning module 268 will update its internal machine learning algorithmsand related predictions (e.g. pump motor energy consumption, pumpdifferential sensor pressure setpoints, etc.) based on the feedbackreceived from the system feedback module 266.

Using an approach similar to the above, the machine learning module 268may also use each individual component prediction as a data set and/ortraining set to make predictions for the energy consumption of theentire heating plant. For example, the machine learning module 268 maybuild a training set comprised of the plant heating hot water flow,plant entering heating hot water temperature, plant leaving heating hotwater temperature, pump head and associated pump motor energyconsumption. Once the training set is of useful size, the machinelearning module 268 may use it to predict pump motor energy consumptionon new data that is not a part of the training set.

The machine learning module 268 may use the training sets describedabove to operate the heating plant as efficiently as possible. Themachine learning module 268 may predict the entering water temperatures,leaving water temperatures, hot water flow, quantity of boilers,quantity and speed of pumps required to operate to satisfy buildingloads based on the predictions received from the hot water systemcoordinator 18.

The system analysis and control module 270 is responsible for thefollowing:

-   -   Monitoring and control of all sensors and actuators related to        the hot water system.    -   Collection of information from the data filtering module 264.        The type of information that this module will collect is        described under the data filtering module 264.    -   Analyzing the data from each sensor and actuator. For example,        the system analysis and control module 270 may determine that a        pump is no longer capable of running (e.g. the motor has burned        out). As such, the system analysis and control module 270 may        need to update its valid scenario (e.g. how many pumps need to        operate and at what speed) to deliver the required amount of        water flow to the zones.    -   Sending the valid system scenarios to the machine learning        module 268 for predictions.    -   Receiving the predictions from the machine learning module 268        and sending commands to the various actuators. The commands that        the system analysis and control module 270 may send to the        actuators could be valve position, damper position or fan speed.    -   Analyzing the scenarios proposed by the scenario generator        module 272. For each scenario received, the system analysis and        control module 270 will analyze the status of the current        sensors and actuators and will determine which scenario is        valid.

The scenario generator module 272 is responsible for continuouslylooking for ways/scenarios to improve the overall energy performance ofthe heating system. For example, the scenario generator module 272 maycreate a series of scenarios which will then be sent to the systemanalysis and control module 270 to analyze and validate. Once thescenarios are validated, they may be sent to the machine learning module268 to make predictions on. The predictions made by the machine learningmodule 268 will then be sent back to the scenario generator module 272for analysis. After analyzing the predictions, the scenario generatormodule 272 may decide to send such predictions to the hot water systemcoordinator 18, which will then be sent to SMITHGROUP-AI 10, which inturn may direct the hot water system coordinator 18 to implement one ofthe scenarios created by scenario generator module 246 of the hot watersystem coordinator 18.

The scenario generator module 268 may create scenarios by varying theheating hot water flow and related temperatures through the boilers orby varying the number of operating pumps. Each such scenario will havean impact on both pump motor energy performance and boiler efficiency.

4. Sample Process

A sample process through which the hot water system coordinator 18 makespredictions related to the overall heating hot water system energyconsumption levels or heating hot water system flows and associatedtemperatures is discussed below.

-   -   AHU system coordinators 20, 22 and zone agents 222, 224, 226,        228, 230, 232, 234 filter the predicted scenarios that are        achievable within their own system. zone agents 222, 224, 226,        228, 230, 232, 234 may then send heating scenario setpoints        (e.g. heating loads, entering water temperatures) to the hot        water system coordinator 18, through the associated AHU system        coordinator 18. This will be considered “raw data” for the hot        water system coordinator 18.    -   The hot water system coordinator 18 may group all scenarios by        the entering water temperature, also known as the temperature        the heating plant must provide. During this step, scenarios with        temperatures that cannot satisfy all zones will be eliminated.    -   The hot water system coordinator 18 will create each scenario        that could occur at the same time. For instance, Zones 1, 2, 3,        4, and 5 must have the same supply air temperature (SAT) as it        will be served by the same air handling unit. the hot water        system coordinator 18 will create these grouped scenarios and        assign a hot water system ID to each one.    -   The hot water system coordinator 18 will summarize all grouped        scenarios and may predict the overall heating hot water system        flow and heating hot water plant return temperature associated        with each scenario. These predictions may then be sent to the        heating plant agent 220. The heating plant agent 220 will then        make predictions on the heating plant energy consumption for        each scenario and will send these predictions back to the hot        water system coordinator 18.    -   The hot water system coordinator 18 may then predict the overall        heating hot water system energy consumption levels for each        scenario and may send these predictions to SMITHGROUP-AI 10. See        SMITHGROUP-AI narrative for additional information.

In further detail, each zone agent creates a series of scenariosassociated with the zone that it is serving and sends these scenarios toa system coordinator (e.g. hot water system coordinator 18). Thesescenarios are given a unique ID number (Table 6 below) based on aspecific airflow supply air temperature (SAT) and a specific enteringwater temperature (EWT).

TABLE 6 Supply Air Temperature Heating Zone SAT Coil Load EWT LWTPrediction ID [° F.] Btu/hr [° F.] [° F.] GPM Agent_1-3_10 52 7500 160153 2.14 Agent_2-3_10 52 12500 160 153 3.57 Agent_3-3_10 52 6000 160 1531.71 Agent_4-3_10 52 15000 160 153 4.29 Agent_5-3_10 52 9800 160 1532.80 Agent_1-4_10 53 5000 160 153 1.43 Agent_2-4_10 53 11000 160 1533.14 Agent_3-4_10 53 5500 160 153 1.57 Agent_4-4_10 53 13000 160 1533.71 Agent_5-4_10 53 9200 160 153 2.63

Each system agent that serves a system that uses hot water creates aseries of scenarios associated with the system that it is serving andsends these scenarios to a system coordinator (e.g. hot water systemcoordinator 18). These scenarios are given a unique ID number based on aspecific airflow supply air temperature (SAT) and a specific enteringwater temperature (EWT).

The hot water system coordinator then filters the scenarios that itreceives (Table 7 below). The filtering process is intended to eliminatescenarios that are not valid for the hot water system at a point intime. For example, an agent may accept a certain cooler hot watertemperature (e.g. 155° F. or below), however due to various systemconditions, the heating water plant may not be able to achieve such acooler hot water temperature. This will render the associated scenariosas invalid.

TABLE 7 Supply Air Temperature Heating Zone SAT Coil Load EWT LWTPrediction [° F.] Btu/hr [° F.] [° F.] GPM Agent_1-3_10 52 7500 160 1532.14 Agent_2-3_10 52 12500 160 153 3.57 Agent_3-3_10 52 6000 160 1531.71 Agent_4-3_6 52 15000 140 132 3.75 Agent_5-3_6 52 9800 140 132 2.45Agent_1-4_6 53 5000 140 133 1.43

The hot water system coordinator 18 will then start creatingcombinations of agent scenarios (Table 8 below). Each combination ofscenarios is then given a unique ID number (e.g. HWS_160-1). For eachcombination of agent scenarios there is a corresponding set of hot waterplant conditions that need to be achieved (e.g. total heating load,entering water temperature (EWT), leaving water temperature (LWT)m andhot water flow (in GPM)).

TABLE 8 Supply Air Heating Coil Zone Hot Water Temperature Load EWT LWTPrediction ID System ID SAT [° F.] Btu/hr [° F.] [° F.] GPM Agent_1-3_10HWS_160-1 52 7500 160 153 2.14 Agent_2-3_10 52 12500 160 153 3.57Agent_3-3_10 52 6000 160 153 1.71 Agent_4-3_10 52 15000 160 153 4.29Agent_5-3_10 52 9800 160 153 2.80 Zone_6-1_3 80 2100 160 150 0.42Zone_7-1_1 84 1100 160 155 0.44 Total 54000 160 153.0 15.37-

The hot water system coordinator 18 then makes hot water system energyconsumptions predictions for each combination of agent scenarios (Table9 below).

TABLE 9 PREDICTION RECEIVED DATA Hot Water Heating Energy Hot Water CoilLoad EWT LWT Consumption System ID Btu/hr [° F.] [° F.] GPM [kWh]HWS_160-1 54000 160 153.0 15.37 HWS_160-2 46900 160 153.0 13.35HWS_160-3 41900 160 152.8 7.90 HWS_160-4 36200 160 153.0 7.46 HWS_160-554300 160 153.0 15.45 HWS_160-6 54400 160 152.8 15.19 HWS_160-7 54400160 153.0 15.45 HWS_160-8 54500 160 153.0 15.49 HWS_160-9 54600 160152.8 15.23 HWS_180-10 36200 180 174.0 12.04 HWS_180-11 36400 180 174.012.12 HWS_180-12 36600 180 174.0 12.2

F. Chilled Water System

1. General Description

Referring to FIG. 16, a chilled water system is shown consisting of fourchilled water pumps 274, 276, 278, 280, pumped in parallel, onewaterside economizer heat exchanger 282, and three chillers 284, 286,288. The distribution piping system consists of two air handling units290, 292 and three thermal zones 294, 296, 298 with chilled water coils.

The control of the entire chilled water system is performed through aseries of independent software agents such as the chilled water systemcoordinator 16, chilled water plant agent 300, and zone agents 302, 304,306.

2. Chilled Water System Coordinator

a. Purpose

The chilled water system coordinator 16 is an independent software agentthat monitors and controls all agents associated with the chilled watersystem. FIG. 17 shows the communication architecture between the variouschilled water system agents.

The chilled water system coordinator 16 is responsible for, but notlimited to, the following:

-   -   Satisfy and maintain all building cooling load requirements.    -   Optimize chilled water system energy usage.    -   Predict energy use of the entire chilled water system.    -   Communication with SMITHGROUP-AI 10. It sends overall chilled        water system energy use predictions and receives commands from        SMITHGROUP-AI 10.    -   Communication with the chilled water plant agent 300. It        requests energy consumption predictions and status from the        chilled water plant agent 300 and sends predictions (e.g. total        chilled water system water flow, chilled water supply        temperature, chilled water return temperature) such that the        chilled water plant agent 300 can make associated predictions        related to the energy performance of the chilled water plant.

Further, the chilled water system coordinator 16, by analyzing thecooling load, water flow and temperature predictions received from thevarious zone agents 304, 306, 308 and AHU system coordinators 20, 22,may predict that the chillers are not required to be enabled and thatthe cooling coils from each AHU 290, 292 may be used to reject the heatabsorbed by the chilled water system from the various zones 294, 296,298. The chilled water system coordinator 16 will then send thesepredictions to SMITHGROUP-AI 10. For example, by analyzing thepredictions from each zone agent 302, 304, 306 and AHU systemcoordinator 20, 22, the chilled water system coordinator 16 maydetermine that a 65° F. temperature will be sufficient to satisfy allcooling loads, without requiring the use of a chiller to cool the water.This can be accomplished by using the chilled water coils from the AHU290, 292 as heating coils. The energy absorbed by the chilled watercoils serving the zone agents 302, 304, 306 is transferred to theairstream within each AHU 290, 292 via its associated chilled watercoils. Similarly, the chilled water system coordinator 16, by analyzingthe cooling load, water flow and temperature predictions received fromthe various zone agents 302, 304, 306 and AHU system coordinators 20,22, may predict that the chillers are only partially required to coolthe chilled water.

b. Internal Structure

Referring to FIG. 18, the internal structure of the CHWS coordinator 16and its related environment is shown. The environment for the chilledwater system coordinator 16 consists of all the agents that it monitorsand controls. The agents that the chilled water system coordinator 16controls are all zones/AHUs that contain a chilled water coil andrequire chilled water from the chilled water system 16. For example, twoair handling unit agents 20, 22 and three zone agents 302, 304, 306 areshown. The agent is comprised of five modules, each with dedicatedalgorithms and control logic.

The data filtering module 310 is responsible for separating the datareceived from the various associated agents 20, 22, 302, 304, 306. Forexample, the actual agent energy consumption levels or actual agentchilled water flow may be sent to the system feedback module 312, whilepredictions from the agents will be sent to the system analysis andcontrol module 314.

The system feedback module 312 is responsible for the following:

-   -   Collection of information from the zone agents 302, 304, 306 and        the chilled water plant agent 300; the information may represent        status (e.g. failed, maintenance), or actual agent energy        consumptions levels from the various agents. A failed status may        indicate that a zone agent 302, 304, 306 may no longer control        its environment and should be excluded from the overall        condenser water system predictions that the machine learning        module 316 is making. A maintenance status may indicate that a        zone agent 302, 304, 306 has entered or may enter into a        maintenance mode and that the machine learning module 316 needs        to update its overall chilled water system predictions        accordingly. The information may also represent predictions        related to the energy consumption levels of each agent 300, 302,        304, 306.    -   Analysis of predictions related to the total chilled water flow.        The actual chilled water flow from the various agents 300, 302,        304, 306 will be compared against their own predictions but also        against the predictions made by the machine learning module 316.        Any large discrepancies between the predicted and actual chilled        water flows could mean that the previous command that a zone        agent 302, 304, 306 or the chilled water plant agent 300        received from the chilled water system coordinator 310 has        placed an agent in conflict with its own internal laws (e.g. not        able to meet the various zone thermal requirements or expected        chilled water system flow). This could indicate that the        difference in the error or accuracy of the predictions made by        the machine learning module 316 and the actual outcome might        have exceeded critical limits, which in turn has affected the        overall energy and thermal performance of the chilled water        system. The system feedback module 312 will then send the actual        chilled water flows to the machine learning module 316 such that        the machine learning module 316 can update its internal learning        algorithms.    -   Collection of information from SMITHGROUP-AI 10. The information        may represent commands that need to be distributed to the        various associated agents. The information may also represent        feedback regarding the predictions that the chilled water system        coordinator 16 has made. For example, the predicted energy        performance of the scenario that SMITHGROUP-AI 10 has directed        the chilled water system coordinator 310 to implement is        significantly different that the real outcome. As such the        system feedback module 312 will pass this feedback to the        machine learning module 316 to update its machine learning        algorithms accordingly such that the accuracy or error of its        predictions self-improves.

The machine learning module 316 is responsible for the following:

-   -   Collection of information from the system feedback module 312.        The type of information that this module will collect is        described under the system feedback module 312.    -   Collection of information from the system analysis and control        module 314. This information may represent predictions from zone        agents 302, 304, 306 and chilled water plant agent 300 regarding        chilled water flows, temperatures, etc.    -   Making predictions, using machine learning algorithms, based on        the data received from the system analysis and control module        314, for the overall energy consumption of the entire chilled        water system or for the total chilled water flow that chilled        water plant agent 300 will need to deliver.    -   Sending the predictions to the system analysis and control        module 314 and to the scenario generator module 318.

The system analysis and control module 314 is responsible for thefollowing:

-   -   Collection of information from the data filtering module 310.        The type of information that this module will collect is        described under the data filtering module 310.    -   Analyzing the data from each agent, and filtering and compiling        the data into valid system scenarios.    -   Sending the valid system scenarios to the machine learning        module 316 for predictions.    -   Receiving the predictions from the machine learning module 316        and sending commands to the various agents. The commands that        the system analysis and control module 314 may send to the        agents could be chilled water flows and associated temperatures        or requests for predictions.    -   Analyzing the scenarios proposed by the scenario generator        module 318. For each scenario received, the system analysis and        control module 314 may decide to direct the various agents to        make predictions and then compile these predictions into valid        system scenarios for the machine learning module 316 to make        predictions on.

The scenario generator module 318 is responsible for continuouslylooking for ways to improve the overall energy performance of the entirechilled water system. For example, the scenario generator module 318 maycreate a series of scenarios which will then be sent to the systemanalysis and control module 314 to analyze and validate. The systemanalysis and control module 314 may ask the associated agents to makepredictions on the scenarios. Once the scenarios are validated, they maybe sent to the machine learning module 316 to make predictions on. Thepredictions made by the machine learning module 316 will then be sentback to the scenario generator 314 module for analysis. After analyzingthe predictions, the scenario generator module 318 may decide to sendsuch predictions to SMITHGROUP-AI 10, which in turn may direct thechilled water system coordinator 16 to implement one of the scenarioscreated by the scenario generator module 318.

The scenario generator module 318 may create scenarios by modelling thechilled water plant agent 300 as delivering various chilled water flowsand associated temperatures, by modelling the zone agents 302, 304, 306or AHU agents as satisfying their load conditions under variousconditions, or by modelling the condenser water plant equipment as beingable to support the chillers to generate such chilled water flows andassociated temperatures.

3. Chilled Water Plant Agent

a. Purpose

The chilled water plant agent 300 is an independent software agent thatmonitors and controls the equipment in the chilled water plant equipment(e.g. chillers, water side economizer, chilled water pumps) as energyefficient as possible while satisfying all cooling load requirements.Further, the chilled water plant agent 300 is responsible for thefollowing:

-   -   a. Prediction of the energy use of the entire chilled water        plant.    -   b. Prediction of the chillers' energy consumption.    -   c. Prediction of the chilled water pumps energy consumption.    -   d. Prediction of the water pressure drop through each of the        chilled water system piping components (e.g. control valve, heat        exchanger, coil).    -   e. Communication with the chilled water system coordinator 16.        It receives valid scenario predictions (e.g. total chilled water        coil loads, chilled water supply temperature, chilled water        return temperature, total chilled water flow) such that the        chilled water plant agent 300 can make associated energy        predictions to send back to the chilled water system coordinator        16.    -   f. Communication with the condenser water coordinator 308. The        type of information that the chilled water plant agent 300 may        communicate with the condenser water system coordinator 308 may        represent valid scenarios or conditions (e.g., minimum and        maximum condenser water return temperature, minimum and maximum        condenser water supply temperature, minimum and maximum        condenser water flow) that the chilled water plant agent 300        needs to account for when making its own energy consumption        predictions. Other information that the condenser water system        coordinator 308 may communicate with the chilled water plant        agent 300 are predictions related to the condenser water flow,        and condenser water return temperature associated with each        piece of equipment that is a part of the chilled water plant        (e.g. chiller, water side economizer).

b. Internal Structure

Referring to FIG. 19, the internal structure of the chilled water plantagent 300 and its related environment is shown. The environment for thechilled water system agent 300 is comprised of all the sensors 320, 322,324, 326 and actuators 328, 330, 332, 334 of the equipment that itmonitors and controls. Sensors 320, 322, 324, 326 and actuators 328,330, 332, 334 that are part of the chilled water system agent'senvironment are connected directly to the network, without the use ofproprietary controllers that operate with programmed sequences ofoperation. In some instances, an open source non-proprietaryinput/output module or a gateway may be required to convert the signalfrom a sensor or an actuator such that it can be communicated via opensource networks such as BACnet, LONworks, Modbus, etc. The agent 300 iscomprised of five modules, each with its own dedicated algorithms andcontrols logic.

The data filtering module 336 is responsible for separating the datareceived from sensors 320, 322, 324, 326 and actuators 328, 330, 332,334. For example, the actual energy consumption levels of the chillersand pumps (measured by their related variable frequency drives (VFDs)),actual pump water flows (measured by the associated flow meters) orvarious temperatures will be sent to the system feedback module 338,while data from other various sensors 320, 322, 324, 326 and actuators(e.g. temperatures, status, valve position, differential pressures etc.)328, 330, 332, 334 will be sent to the system analysis and controlmodule 340.

The system feedback module 338 is responsible for the following:

-   -   Collection of information from the various sensors 320, 322,        324, 326 and actuators 328, 330, 332, 334. The type of        information that the system feedback module 338 may collect is        described in the data filtering module 336.    -   Analysis of predictions related to various water temperatures        and flows (e.g. entering and leaving chilled water temperatures,        entering and leaving condenser water temperatures, chilled water        flow, condenser water flow, etc.). The data received from the        various sensors 320, 322, 324, 326 and actuators 328, 330, 332,        334 will be compared against the predictions made by the machine        learning module 342. Any large discrepancies between the        predicted and measured values could mean that the previous        commands could indicate that the difference in the error or        accuracy of the predictions made by the machine learning module        342 and the actual outcome might have exceeded critical limits,        which in turn has affected the overall energy and energy        performance of the chilled water system. The system feedback        module 338 will then send the measured values to the machine        learning module 342 such that the machine learning module 342        can update its internal learning algorithms.    -   Collection of information from the chilled water system        coordinator 16. The information may represent feedback regarding        the predictions that the chilled water plant agent 300 has made.        For example, the chilled water plant might deliver a certain        chilled water flow to the zones 294, 296, 298 or to the AHUs        290, 292 in the same time its internal flow meters are        confirming that the measured chilled water flow is equal to or        close to what was predicted. However, some zones 294, 296, 298        or AHUs 290, 292, as measured by their associated flow meters,        might not receive sufficient chilled water. This could be        indication that the chilled water flow meters within the chilled        water plant are defective or that they may need recalibration.

The machine learning module 342 is responsible for the following:

-   -   Collection of information from the system feedback module 338.        The type of information that this module will collect is        described under the system feedback module 338.    -   Collection of information from the system analysis and control        module 340. This information may represent data from the various        sensors 320, 322, 324, 326 and actuators 328, 330, 332, 334,        such as water flows and temperatures.    -   Making predictions, using machine learning algorithms, based on        the data received from the system analysis and control module        340 or from the system feedback module 338.    -   Sending the predictions to the system analysis and control        module 340 and to the scenario generator module 344.

Using manufacturers rated chiller performance and chiller curves data asa training set, a machine learning algorithm may be trained to predictthe chiller energy consumption at various system conditions (e.g.entering and leaving chilled water temperatures, entering and leavingcondenser water temperatures, chiller water flows, condenser water flowsetc.). Once released into the real environment, the machine learningmodule 342, via the feedback received from the system feedback module338, may update its internal machine learning algorithms and relatedpredictions to account for system effects and the measurement errors ofthe various sensors.

Similarly, using pump laws and manufacturer's rated pump performancedata as a training set, a machine learning algorithm may be trained topredict the chilled water pump energy consumption at various systemconditions, water flow, and pump head conditions. Once released into thereal environment, the machine learning module 342, via the feedbackreceived from the system feedback module 338, may update its internalmachine learning algorithms and related predictions (e.g. pump motorenergy consumption, pump differential sensor pressure setpoints etc.) toaccount for system effects and the measurement errors of the varioussensors.

Using an approach like the above, the machine learning module 342 mayalso use each individual component prediction as a data set and/ortraining set to make predictions for the energy consumption of theentire chilled water plant. For example, the machine learning module 342may build a training set comprised of the plant chilled water flow,plant entering chilled water temperature, plant leaving chilled watertemperature, and pump head and associated pump motor energy consumption.Once the training set is of useful size, the machine learning module 342may use it to predict pump motor energy consumption on new data that isnot a part of the training set.

The machine learning module 342 will use the training sets describedabove to operate the chilled water plant as efficiently as possible. Themachine learning module 342 may predict the entering water temperatures,leaving water temperatures, chilled water flow, quantity of chillers,and quantity and speed of chilled water pumps that are required tooperate to satisfy building loads based on the predictions received fromthe chilled water system coordinator 16.

The system analysis and control module 340 is responsible for thefollowing:

-   -   Monitoring and control of all sensors and actuators related to        the chilled water system.    -   Collection of information from the data filtering module 336.        The type of information that this module will collect is        described under the data filtering module 336.    -   Analyzing the data from each sensor and actuator. For example,        the system analysis and control module 340 may determine that a        pump is no longer capable of running (e.g. the motor has burned        out). As such, the system analysis and control module 340 may        need to update its valid scenario (e.g. how many pumps need to        operate and at what speed) to deliver the required amount of        water flow to the zones 294, 296, 298.    -   Sending the valid system scenarios to the machine learning        module 342 for predictions.    -   Receiving the predictions from the machine learning module 342        and sending commands to the various actuators 328, 330, 332,        334. The commands that the system analysis and control module        340 may send to the actuators 328, 330, 332, 334 could be valve        position or damper position or pump speed.    -   Analyzing the scenarios proposed by the scenario generator        module 344. For each scenario received, the system analysis and        control module 340 will analyze the status of the current        sensors 320, 322, 324, 326 and actuators 328, 330, 332, 334 and        will determine which scenario is valid. For example, one of the        scenarios that the scenario generator module 344 may generate        will require the chilled water plant to deliver 1,000 GPM to the        chiller. The system analysis and control module 340 may        determine that, due to a pump failure, such scenario is no        longer valid and that the machine learning module 342 will not        make a prediction on the referenced scenario.

The scenario generator module 344 is responsible for continuouslylooking for ways/scenarios to improve the overall energy performance ofthe chilled water plant. For example, the scenario generator module 344may create a series of scenarios which will then be sent to the systemanalysis and control module 340 to analyze and validate. Once thescenarios are validated, they may be sent to the machine learning module342 to make predictions on. The predictions made by the machine learningmodule 342 will then be sent back to the scenario generator module 344for analysis. After analyzing the predictions, the scenario generatormodule 344 may decide to send such predictions to the chilled watersystem coordinator 16, which will then be sent to SMITHGROUP-AI 10,which in turn may direct the chilled water system coordinator 16 toimplement one of the scenarios created by the chilled water systemscenario generator module 344. The scenario generator module 344 maycreate scenarios by varying the chilled water flow and relatedtemperatures to the chillers. Each such scenario will have an impact onboth pump motor energy performance and chiller efficiency.

4. Sample Process

Each AHU coordinator 290, 292 and zone agent 294, 296, 298 comprised inthe chilled water system coordinator's environment will send a series ofpredictions and status information. Predictions that the chilled watersystem coordinator 16 will receive may include the cooling coil load,entering and leaving water temperatures, and various GPMs that the zoneagents 302, 304, 306 or AHU agents could use to satisfy its zonerequirements. The system analysis and control module 314 will firstcompile all predictions received from the zone agents 302, 304, 306 andAHU agents. The system analysis and control module 314 will theneliminate all predictions that will make other agents incapable ofmeeting their internal laws. For example, all scenarios with EWT greaterthan 46° F. can be eliminated because AHU-1 290 and AHU-2 292 cannotsatisfy load requirements with these water temperatures. The systemanalysis and control module 314 will then compile and sort all remainingpredictions based on a common chilled water coil entering watertemperature.

The system analysis and control module 314 will then generate allpossible combinations of entering water temperatures and total GPMs andsummarize scenarios to be sent to the chilled water system agent 300.The chilled water plant agent 300 will then determine the most efficientoperating scenarios and predict condenser water entering and leavingwater temperatures and condenser water flow. The chilled water agent 300will send these predictions to be validated by the condenser watersystem coordinator 16. Once validated, the chilled water plant agent 300will then send the final validated scenarios to the chilled water systemcoordinator 16 which will then send them to SMITHGROUP-AI 10.

In more detail, each zone agent creates a series of scenarios associatedwith the zone that it is serving and sends these scenarios to a systemcoordinator (e.g. chilled water system coordinator 16). These scenariosare given a unique ID number (Table 10 below) based on a specificairflow supply air temperature (SAT) and a specific entering watertemperature (EWT).

TABLE 10 Zone Agent-9 AGENT PREDICTIONS Data Provided by the Zone AgentCooling Zone Supply Air Coil Energy Prediction Temperature Load EWT LWTPredictions ID SAT [° F.] Btu/hr [° F.] [° F.] GPM KWh Agent_9-1-1 5024000 36 44 6.00 0.4 Agent_9-1-2 50 24000 38 49 4.36 0.4 Agent_9-1-3 5024000 40 47 6.86 0.4 Agent_9-2-1 52 21000 36 48 3.50 0.6 Agent_9-2-2 5221000 38 53 2.80 0.6 Agent_9-2-3 52 21000 40 51 3.82 0.6

Similarly, each system agent, creates a series of scenarios associatedwith the system that it is serving and sends these scenarios to a systemcoordinator (e.g. chilled water system coordinator 16). These scenariosare given a unique ID number (Table 11 below) based on a specificairflow supply air temperature (SAT) and a specific entering watertemperature (EWT).

TABLE 11 AHU-1 AGENT PREDICTIONS Data Provided by the AHU SystemCoordinator Supply Air Temperature Cooling AHU System SAT Coil Load EWTLWT Prediction ID [° F.] Btu/hr [° F.] [° F.] GPM AHU_1-1-3 52 114480 4055 15 AHU_1-1-4 52 114480 42 54 19 AHU_1-1-5 52 114480 44 51 33AHU_1-1-6 52 114480 46 56 23 AHU_1-2-3 54 116964 40 48 29 AHU_1-2-4 54116964 42 53 21 AHU_1-2-5 54 116964 44 55 21

The chilled water system coordinator then filters the scenarios that itreceives (Table 12 below). The filtering process is intended toeliminate scenarios that are not valid for the chilled water system at apoint in time. For example, an agent may accept a certain low chilledwater temperature (e.g. 38° F. or below), however due to outside airtemperature conditions, the chilled water plant may not be able toachieve such low chilled water temperature. This will render theassociated scenarios as invalid (as indicated in bold).

TABLE 12 Zone Agent-9 AGENT PREDICTIONS Data Provided by the Zone AgentCooling Zone Supply Air Coil Energy Prediction Temperature Load EWT LWTPredictions ID SAT [° F.] Btu/hr [° F.] [° F.] GPM KWh Agent_9-1-1 5024000 36 44 6.00 0.4 Agent_9-1-2 50 24000 38 49 4.36 0.4 Agent_9-1-3 5024000 40 47 6.86 0.4 Agent_9-2-1 52 21000 36 48 3.50 0.6 Agent_9-2-2 5221000 38 53 2.80 0.6 Agent_9-2-3 52 21000 40 51 3.82 0.6

After the filtering process is complete, the chilled water systemcoordinator 16 compiles all valid scenarios (Table 13 below) that willbe used to predict the energy consumption of the chilled water system.It then sorts these scenarios based on a common chilled watertemperature (e.g. 40° F.).

TABLE 13 CHILLED WATER SYSTEM AGENT PREDICTIONS Supply Air TemperatureCooling AHU System SAT Coil Load EWT LWT Prediction ID [° F.] Btu/hr [°F.] [° F.] GPM AHU_1-1-3 52 114480 40 55 15.26 AHU_1-2-3 54 116964 40 4829.24 AHU_1-3-3 56 134136 40 46 44.71 AHU_2-1-3 50 240000 40 54 34.29AHU_2-2-3 52 180000 40 47 51.43 AHU_2-3-3 54 120000 40 51 21.82Agent_8-1-3 50 15000 40 55 2.00 Agent_8-2-3 52 12000 40 47 3.43Agent_8-3-3 54 10000 40 51 1.82 Agent_8-4-3 56 8000 40 50 1.60Agent_9-1-3 50 24000 40 47 6.86 Agent_9-2-3 52 21000 40 51 3.82Agent_9-3-3 54 18000 40 48 4.50 Agent_9-4-3 56 15000 40 50 3.00

The chilled water system coordinator 16 will then start creatingcombinations of agent scenarios (Table 14 below). Each combination ofscenarios is then given a unique ID number (e.g. CHWS_40-1). For eachcombination of agent scenarios there is a corresponding set of chilledwater plant conditions that need to be achieved (e.g. total coolingload, entering water temperature (EWT), leaving water temperature (LWT)and chilled water flow (in GPM)).

TABLE 14 CHILLED WATER SYSTEM AGENT PREDICTIONS System IdentificationData Predictions Chilled Water Supply Air Cooling AHU/Zone System SystemPredition Temperature Coil Load EWT LWT Prediction ID ID SAT [° F.]Btu/hr [° F.] [° F.] GPM AHU_1-1-3 CHWS_40-1 52 114480 40 55 15.26AHU_2-1-3 50 240000 40 54 34.29 Agent_8-4-3 56 8000 40 50 1.60Agent_9-3-3 54 18000 40 48 4.50 Total 380480 40 53.7 55.65

The chilled water system coordinator 16 then makes chilled water systemenergy consumptions predictions for each combination of agent scenarios(Table 15 below) and a unique set of condenser water system operatingconditions.

TABLE 15 CHILLED WATER SYSTEM AGENT PREDICTIONS System IdentificationPredictions Data Energy Consumption Chilled Water CHWS Pump CoolingCondenser Water Energy CHWS Energy CHWS Total Energy Chilled Water CoilLoad EWT LWT EWT LWT Consumption Consumption Consumption SystemPredition ID Btu/hr [° F.] [° F.] GPM [° F.] [° F.] GPM KWh KWh KWhCHWS_40-1-1 380480 40 53.7 55.65 85 97 63.4 25 324 349 CHWS_40-1-2380480 40 53.7 55.65 80 93 58.5 25 302 327 CHWS_40-1-3 380480 40 53.755.65 75 88 58.5 25 278 303

The chilled water system energy consumptions predictions are thenvalidated by the condenser water system coordinator 308 (e.g. Table 16below). This filtering/validation process is required due to the factthat, even though the chilled water plant is able to meet the associatedconditions (e.g. chilled water EWT, LWT, GPM, etc.), the condenser watersystem may not be able to provide/support the various condenser watersystem operating conditions (e.g. condenser water EWT, LWT, GPM, etc.).For example, if the outside air temperature is about 95° F., thecondenser water system may not be able to achieve a LWT of 80° F. orbelow.

TABLE 16 CHILLED WATER SYSTEM AGENT PREDICTIONS System IdentificationPredictions Data Energy Consumption Chilled Water CHWS Cooling Pump CHWSCHWS Total Chilled Water Coil Condenser Water Energy Energy EnergySystem Load EWT LWT EWT LWT Consumption Consumption ConsumptionPrediction ID Btu/hr [° F.] [° F.] GPM [° F.] [° F.] GPM KWh KWh KWhCHWS_40-1-1 380480 40 53.7 55.65 85 97 63.4 25 324 349 CHWS_40-1-2380480 40 53.7 55.65 80 93 58.5 25 302 327 CHWS_40-1-3 380480 40 53.755.65 75 88 58.5 25 278 303 CHWS_40-1-4 380480 40 53.7 55.65 70 80 76.125 265 290 CHWS_40-1-5 380480 40 53.7 55.65 65 76 69.2 25 247 272CHWS_40-1-6 380480 40 53.7 55.65 60 69 84.6 25 224 249 CHWS_40-1-7380480 40 53.7 55.65 55 65 76.1 25 209 234

After the chilled water system coordinator 16 has received confirmationfrom the condenser water system coordinator 308, it then sends thevalidated scenario combinations to SMITHGROUP-AI 10.

G. Condenser Water System

1. General Description

Referring to FIG. 20, a condenser water system is shown as having fourcondenser water pumps 346, 348, 350, 352, and three cooling towers, eachwith two cells 354, 356, 358, 360, 362, 364. Condenser water isdelivered to the chilled water plant equipment (e.g. chillers 368, 370,372 and the waterside economizer 374) and to four other zones thatrequire condenser water. The control of the entire condenser watersystem is performed through the condenser water system coordinator 16,chilled water plant agent 300, condenser water plant agent 376, and zoneagents 378, 380, 384, 386.

FIG. 21 shows the communication architecture between the variouscondenser water system agents 300, 376, 378, 380, 384, 386. All sensorsand actuators are connected directly to the network, without the use ofproprietary controllers that operate with programmed sequences ofoperation. in some instances, an open source non-proprietaryinput/output module or a gateway may be required to convert the signalfrom a sensor or an actuator such that it can be communicated via opensource networks such as BACnet, LONworks, Modbus, etc.

2. Condenser Water System Coordinator

a. Purpose

The condenser water system coordinator 16 is an independent softwareagent that monitors and controls all agents associated with itsrespective condenser water system. Further, the condenser water systemcoordinator 16 is responsible for the following:

-   -   Prediction of the energy use of the entire condenser water        system.    -   Communication with SMITHGROUP-AI 10. It sends overall condenser        water system energy use predictions and receives commands from        SMITHGROUP-AI 10.    -   Communication with the condenser water plant agent 376. It        requests energy consumption predictions and status from the        condenser water plant agent 376 and sends predictions (e.g.        total condenser water system water flow, condenser water supply        temperature, condenser water return temperatures) such that the        condenser water plant agent 376 can make associated predictions        related to the energy performance of the condenser water plant.    -   Communication with the chilled water plant agent 382. The type        of information that the condenser water system coordinator 388        communicates to chilled water plant agent 382 may represent        valid scenarios or conditions (e.g., minimum and maximum        condenser water return temperature, minimum and maximum        condenser water supply temperature, minimum and maximum        condenser water flow) that the chilled water plant agent 382        needs to account for when making its own energy consumption        predictions. Other information that the condenser water system        coordinator 388 may communicate with the chilled water plant        agent 382 are predictions related to the condenser water flow,        and condenser water return temperature associated with each        piece of equipment that is a part of the chilled water plant        (e.g. chillers 368, 370, 372, water side economizer 374).

b. Internal Structure

Referring to FIG. 22, the internal structure of the condenser watersystem coordinator 16 and its related environment is shown. Theenvironment for the condenser water system coordinator 16 is comprisedof all the agents that it monitors and controls. The agent is comprisedof five modules, each with its own dedicated algorithms and controlslogic.

The data filtering module 388 is responsible for the following:

-   -   Separating the data received from the various associated agents.        For example, the actual agent energy consumption levels or        actual agent condenser water flow will be sent to the system        feedback module 390, while predictions from the agents will be        sent to the system analysis and control module 392.    -   Collection of information from the chilled water plant agent        382. The type of information that the data filtering module 388        may collect from the chilled water plant agent 300 may represent        prediction scenarios that the chilled water plant agent 382        needs to make and for which it needs validation. For example,        some of the prediction scenarios that the chilled water plant        agent 382 intends on analyzing may require a condenser water        temperature of 85° F. or above. The data filtering module 388        will send these prediction scenarios to the system analysis and        control module 392 for processing and validation.

The system feedback module 390 is responsible for the following:

-   -   Collection of information from the zone agents 378, 380, 384,        386, the condenser water plant agent 376, and the chilled water        plant agent 300. The information may represent status (e.g.        failed, maintenance), or actual agent energy consumptions levels        from the various agents. A failed status may indicate that a        zone agent 378, 380, 384, 386 no longer controls its environment        and should be excluded from the overall condenser water system        predictions that the machine learning module 394 is making. A        maintenance status may indicate that a zone agent 378, 380, 384,        386 has entered or may enter into a maintenance mode and that        the machine learning module 394 needs to update its overall        condenser water system predictions accordingly. The information        may also represent predictions related to the energy consumption        levels of each agent.    -   Analysis of predictions related to the total condenser water        flow. The actual condenser water flow from the various agents        will be compared against their own predictions but also against        the predictions made by the machine learning module 394. Any        large discrepancies between the predicted and actual condenser        water flows could mean that the previous command that a zone        agent 378, 380, 384, 386 or the condenser water plant agent 376        received from the condenser water system coordinator 16 has        placed an agent in conflict with its own internal laws (e.g. not        able to meet the various zone thermal requirements or expected        condenser water system flow). This could indicate that the        difference in the error or accuracy of the predictions made by        the machine learning module 394 and the actual outcome might        have exceeded critical limits, which in turn has affected the        overall energy and thermal performance of the condenser water        system. The system feedback module 390 will then send the actual        condenser water flows to the machine learning module 394 such        that the machine learning module 394 can update its internal        learning algorithms.    -   Collection of information from SMITHGROUP-AI 10. The information        may represent commands that need to be distributed to the        various associated agents. The information may also represent        feedback regarding the predictions that the condenser water        system coordinator 16 has made. For example, the predicted        energy performance of the scenario that SMITHGROUP-AI 10 has        directed the condenser water system coordinator 16 to implement        is significantly different that the real outcome. As such the        system feedback module 390 will pass this feedback to the        machine learning module 394 to update its machine learning        algorithms accordingly such that the accuracy or error of its        predictions self-improves.

The machine learning module 394 is responsible for the following:

-   -   Collection of information from the system feedback module 390.        The type of information that this module will collect is        described under the system feedback module 390.    -   Collection of information from the system analysis and control        module 392. This information may represent predictions from zone        agents 378, 380, 384, 386 and condenser water plant agent 376        regarding condenser water flows, temperatures, etc.    -   Making predictions, using machine learning algorithms, based on        the data received from the system analysis and control module        392, for the overall energy consumption of the entire condenser        water system or for the total condenser water flow that the        condenser water plant agent 376 will need to deliver.    -   Sending the predictions to the system analysis and control        module 392 and to the scenario generator module 396.

The system analysis and control module 392 is responsible for thefollowing:

-   -   Collection of information from the data filtering module 388.        The type of information that this module will collect is        described under the data filtering module 388.    -   Analyzing the data from each agent, and filtering and compiling        the data into valid system scenarios.    -   Sending the valid system scenarios to the machine learning        module 394 for predictions.    -   Receiving the predictions from the machine learning module 394        and sending commands to the various agents. The commands that        the system analysis and control module 392 may send to the        agents could be condenser water flows and associated        temperatures, or requests for predictions.    -   Analyzing the scenarios proposed by the scenario generator        module 396. For each scenario received, the system analysis and        control module 392 may decide to direct the various agents to        make predictions and then compile these predictions into valid        system scenarios for the machine learning module 394 to make        predictions on.    -   Analyzing the scenarios proposed by the scenario generator        module 396 that require input from the chilled water plant agent        382. For each scenario received, the system analysis and control        module 392 may decide to ask the chilled water plant agent 382        to make predictions and then send the predictions back to the        data filtering module 388.

The scenario generator module 396 is responsible for continuouslylooking for ways to improve the overall energy performance of the entirecondenser water system. For example, the scenario generator module 396may create a series of scenarios that will then be sent to the systemanalysis and control module 392 to analyze and validate. The systemanalysis and control module 392 may ask the agents to make predictionson the scenarios. Once the scenarios are validated, they may be sent tothe machine learning module 394 to make predictions on. The predictionsmade by the machine learning module 394 will then be sent back to thescenario generator module 396 for analysis. After analyzing thepredictions, the scenario generator module 396 may decide to send suchpredictions to SMITHGROUP-AI 10, which in turn may direct the condenserwater system coordinator 16 to implement one of the scenarios created bythe scenario generator module 396.

The scenario generator module 396 may create scenarios by modelling thecondenser water plant agent 376 as delivering various condenser waterflows and associated temperatures and by modelling the zone agents 378,380, 384, 386 as satisfying their zone thermal load conditions undervarious conditions or by modelling the chilled water plant equipment asbeing able to support such condenser water flows and associatedtemperatures.

c. Sample Process

The process through which the condenser water system coordinator 16makes predictions related to the overall condenser water system energyconsumption levels or condenser water system flows and associatedtemperatures is similar to the process that the chilled water systemcoordinator 16 is implementing when making predictions related to theoverall chilled water system energy consumption levels or chilled watersystem flows and associated temperatures.

3. Condenser Water Plant Agent

a. Purpose

The condenser water plant agent 376 is an independent software agentthat monitors and controls all sensors 398, 400, 402, 404 and actuators406, 408, 410, 412 associated with the condenser water plant. Thecondenser water plant is comprised of cooling towers and condenser waterpumps. Further, the condenser water plant agent 376 is responsible forthe following:

-   -   Prediction of the energy use of the entire condenser water        plant.    -   Communication with the condenser water system coordinator 16. It        sends overall condenser water plant energy use predictions and        receives commands from the condenser water system coordinator        16.

b. Internal Structure

Referring to FIG. 23, the internal structure of the condenser waterplant agent 376 and its related environment is shown. The environmentfor the condenser water plant agent 376 is comprised of the sensors andactuators that are used to monitor and control the condenser waterplant. The agent is comprised of five modules, each with its owndedicated algorithms and controls logic.

The data filtering module 414 is responsible for separating the datareceived from sensors 398, 400, 402, 404 and actuators 406, 408, 410,412. For example, the energy consumption levels of cooling tower fans orcondenser water pumps (measured by their related variable frequencydrives (VFD)), actual condenser water flow (measured by the associatedflow meter), or various temperatures (e.g. condenser water leavingtemperature, condenser water return temperature, etc.) will be sent tothe system feedback module 416, while data from other various sensors(e.g. alarms, temperatures etc.) will be sent to the system analysis andcontrol module 418. The data filtering module 414 may also send to thesystem analysis and control module 418 the same data that was sent tothe system feedback module 416.

The system feedback module 416 is responsible for the following:

-   -   Collection of information from the various sensors 398, 400,        402, 404 and actuators 406, 408, 410, 412. The type of        information that the system feedback module 416 may collect is        described in the data filtering module 414.    -   Analysis of predictions related to various temperatures (e.g.        condenser water leaving temperature, condenser water return        temperature, etc.). The data received from the various sensors        and actuators will be compared against the predictions made by        the machine learning module 420. Any large discrepancies between        the predicted and measured values could mean that the previous        commands could indicate that the difference in the error or        accuracy of the predictions made by the machine learning module        420 and the actual outcome might have exceeded critical limits,        which in turn has affected the overall energy and energy        performance of the condenser water plant. The system feedback        module 416 will then send the measured values to the machine        learning module 420 such that the machine learning module 420        can update its internal learning algorithms.    -   Collection of information from the condenser water system        coordinator 16. The information may represent feedback regarding        the predictions that the condenser water plant agent 376 has        made. For example, the condenser water plant might deliver a        certain flow of condenser water to the zones or to the chilled        water plant in the same time its internal flow meters are        confirming that the measured condenser water flow is equal to or        close to what was predicted. However, some zones or the chilled        water plant, as measured by their associated flow meters, might        not receive sufficient condenser water. This could be indication        that the water flow meters are defective or that they may need        recalibration.

The machine learning module 420 is responsible for the following:

-   -   Collection of information from the system feedback module 416.        The type of information that this module will collect is        described under the system feedback module 416.    -   Collection of information from the system analysis and control        module 418. This information may represent data from the various        sensors and actuators, such as condenser water flows and        associated temperatures.    -   Making predictions, using machine learning algorithms, based on        the data received from the system analysis and control module        418 or from the system feedback module 416.    -   Sending the predictions to the system analysis and control        module 418 and to the scenario generator module 422.

For each component (e.g. cooling tower, condenser water pump etc.) ofthe condenser water plant, the machine learning module 420 will usevarious machine learning algorithms to predict its performance (e.g.Btu/hr, leaving water temperature etc.), water flow, water pressuredrops, and energy consumption (e.g. kWh).

For example, a cooling tower has fixed properties (e.g. width, height,fan horsepower etc.). As such, a machine learning algorithm may betrained, via the use of manufacturer's rated cooling tower performancedata at various conditions (e.g. various entering wet bulb airtemperatures, entering condenser water temperatures and flow), topredict what the leaving condenser water temperature, or associatedcondenser water pressure drop may be at other conditions not included inthe training data set.

Similarly, using pump laws and manufacturer's rated pump performancedata as a training set, a machine learning algorithm may be trained topredict what the condenser water pump energy consumption may be atvarious system conditions (e.g. various water flows and variousassociated pump head). Once released into the real environment, themachine learning module 420, via the feedback received from the systemfeedback module 416, may update its internal machine learning algorithmsand related predictions (e.g. pump motor energy consumption, pump headsetpoints etc.) to account for system effects and for the measurementerrors of the various sensors.

Using an approach similar to the above, the machine learning module 420may also use each individual component prediction as a data set and/ortraining set to make predictions for the energy consumption of theentire condenser water plant. For example, the machine learning module420 may build a training set comprised of the plant condenser waterflow, plant entering condenser water temperature, plant leavingcondenser water temperature, pump head and associated pump motor energyconsumption. Once the training set is of useful size, the machinelearning module 420 may use it to predict pump motor energy consumptionon new data that is not a part of the training set.

The machine learning module 420 will use the training sets describedabove to operate the condenser water plant as efficiently as possible.The machine learning module 420 may predict the entering watertemperatures, leaving water temperatures, condenser water flow, quantityof towers, and quantity and speed of condenser water pumps that arerequired to operate to satisfy condenser water load requirements basedon the predictions received from the condenser water system coordinator16.

The system analysis and control module 418 is responsible for thefollowing:

-   -   Monitoring and control of all sensors and actuators related to        the condenser water plant.    -   Collection of information from the data filtering module 414.        The type of information that this module will collect is        described under the data filtering module 414.    -   Analyzing the data from each sensor and actuator. For example,        the system analysis and control module 418 may determine that a        cooling tower fan is no longer capable of running (e.g. the        motor has burned out). As such, the system analysis and control        module 418 may need to update its valid scenarios (e.g. how many        cooling tower fans need to operate and at what speed) to deliver        the required condenser water plant temperature and flow to the        zones and to the chilled water plant.    -   Sending the valid system scenarios to the machine learning        module 420 for predictions.    -   Receiving the predictions from the machine learning module 420        and sending commands to the various actuators. The commands that        the system analysis and control module 418 may send to the        actuators could be valve position, cooling tower fan speed, or        condenser water pump speed.    -   Analyzing the scenarios proposed by the scenario generator        module 422. For each scenario received, the system analysis and        control module 418 will analyze the status of the current        sensors and actuators and will determine which scenario is        valid. For example, one of the scenarios that the scenario        generator module 422 may generate will require the condenser        water plant to deliver 2,000 GPM of condenser water at 65° F. to        the zones and to the chilled water plant. The system analysis        and control module 418 may determine that, due to a cooling        tower fan failure or condenser water pump failure, such scenario        is no longer valid (due to limited condenser water plant        capacity) and that the machine learning module 420 will not make        a prediction on the referenced scenario.

The scenario generator module 422 is responsible for continuouslylooking for ways/scenarios to improve the overall energy performance ofthe condenser water plant. For example, the scenario generator module422 may create a series of scenarios that will then be sent to thesystem analysis and control module 418 to analyze and validate. Once thescenarios are validated, they may be sent to the machine learning module420 to make predictions on. The predictions made by the machine learningmodule 420 will then be sent back to the scenario generator module 422for analysis. After analyzing the predictions, the scenario generatormodule 422 may decide to send such predictions to the condenser watersystem coordinator 16, which may send them to SMITHGROUP-AI 10, which inturn may direct the condenser water system coordinator 16 to implementone of the scenarios created by the scenario generator module 16.

The scenario generator module 422 may create scenarios by varying thecooling tower air flows, water flows and related temperatures. Each suchscenario will have an impact on the energy performance of the motorsserving the tower fans or the condenser water pumps.

The algorithms, processes, methods, logic, or strategies disclosed maybe deliverable to and/or implemented by a processing device, controller,or computer, which may include any existing programmable electroniccontrol unit or dedicated electronic control unit. The supervisors,coordinators, and agents contemplated herein may be implemented acrossseveral processors as shown in FIG. 1 or a single processor, etc.Similarly, the algorithms, processes, methods, logic, or strategies maybe stored as data and instructions executable by a controller orcomputer in many forms including, but not limited to, informationpermanently stored on various types of articles of manufacture that mayinclude persistent non-writable storage media such as ROM devices, aswell as information alterably stored on writeable storage media such asfloppy disks, magnetic tapes, CDs, RAM devices, and other magnetic andoptical media. The algorithms, processes, methods, logic, or strategiesmay also be implemented in a software executable object. Alternatively,they may be embodied in whole or in part using suitable hardwarecomponents, such as application specific integrated circuits (ASICs),field-programmable gate arrays (FPGAs), state machines, controllers orother hardware components or devices, or a combination of hardware,software and firmware components.

The words used in the specification are words of description rather thanlimitation, and it is understood that various changes may be madewithout departing from the spirit and scope of the disclosure andclaims. As previously described, the features of various embodiments maybe combined to form further embodiments that may not be explicitlydescribed or illustrated. While various embodiments may have beendescribed as providing advantages or being preferred over otherembodiments or prior art implementations with respect to one or moredesired characteristics, those of ordinary skill in the art recognizethat one or more features or characteristics may be compromised toachieve desired overall system attributes, which depend on the specificapplication and implementation. These attributes include, but are notlimited to cost, strength, durability, life cycle cost, marketability,appearance, packaging, size, serviceability, weight, manufacturability,ease of assembly, etc. As such, embodiments described as less desirablethan other embodiments or prior art implementations with respect to oneor more characteristics are not outside the scope of the disclosure andmay be desirable for particular applications.

What is claimed:
 1. A hierarchical resource management system for abuilding that has a plurality of zones each with a correspondingresource arranged to alter an environment of the zone, the systemcomprising: one or more processors programmed to implement a pluralityof agents each configured to monitor sensed values describing conditionsof one of the zones, generate operating scenarios based on the sensedvalues for the resource corresponding to the one of the zones, each ofthe operating scenarios describing an array of set point values for theresource and corresponding to an energy consumption of the resource, andoperate the resource according to a commanded one of the operatingscenarios, a coordinator configured to, responsive to receipt of theoperating scenarios from each of the agents, filter the operatingscenarios to remove the operating scenarios that violate internal lawsof the agents to form an aggregate validated set of operating scenarios,and a supervisor configured to, responsive to receipt of targetconditions for the zones and the aggregate validated set of operatingscenarios from the coordinator, select a combination of the operatingscenarios from the aggregate validated set of operating scenarios thatachieves the target conditions and minimizes overall energy consumptionby the resources such that some of the operating scenarios of thecombination do not minimize energy consumption of the resourcescorresponding to the some of the operating scenarios, and direct thecoordinator to command operation of the resources according to thecombination of the operating scenarios.
 2. The system of claim 1,wherein the agents are further configured to execute machine learningalgorithms to generate the operating scenarios.
 3. The system of claim1, wherein the supervisor is further configured to execute machinelearning algorithms to select the combination of the operating scenariosfrom the aggregate validated set of operating conditions.
 4. The systemof claim 1, wherein the agents are further configured to report theinternal laws to the coordinator.
 5. The system of claim 1, wherein oneof the zones is a thermal zone and the resource corresponding to thethermal zone is a heating ventilating and air conditioning unit.
 6. Thesystem of claim 1, wherein one of the zones is a lighting zone.
 7. Thesystem of claim 1, wherein one of the zones is an electrical switchedreceptacle zone.